tests/testthat/_snaps/clipboard.md
In tidymodels/usemodels: Boilerplate Code for 'Tidymodels' Analyses
all model templates with clipboard
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_1_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) "
[5] ""
[6] "test_config_1_dummies_spec <- "
[7] " bag_tree() %>% "
[8] " set_mode(\"regression\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_1_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_1_dummies_recipe) %>% "
[14] " add_model(test_config_1_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_1_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) "
[5] ""
[6] "test_config_1_no_dummies_spec <- "
[7] " bag_tree() %>% "
[8] " set_mode(\"classification\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_1_no_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_1_no_dummies_recipe) %>% "
[14] " add_model(test_config_1_no_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_2_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_2_no_dummies_spec <- "
[5] " boost_tree() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"C5.0\") "
[8] ""
[9] "test_config_2_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_2_no_dummies_recipe) %>% "
[12] " add_model(test_config_2_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_3_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_3_dummies_spec <- "
[8] " cubist_rules() %>% "
[9] " set_engine(\"Cubist\") "
[10] ""
[11] "test_config_3_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_3_dummies_recipe) %>% "
[14] " add_model(test_config_3_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_4_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_4_dummies_spec <- "
[5] " bart() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_4_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_4_dummies_recipe) %>% "
[12] " add_model(test_config_4_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_4_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_4_no_dummies_spec <- "
[5] " bart() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_4_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_4_no_dummies_recipe) %>% "
[12] " add_model(test_config_4_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_5_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) "
[10] ""
[11] "test_config_5_dummies_spec <- "
[12] " mars() %>% "
[13] " set_mode(\"regression\") %>% "
[14] " set_engine(\"earth\") "
[15] ""
[16] "test_config_5_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_5_dummies_recipe) %>% "
[19] " add_model(test_config_5_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_5_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) "
[10] ""
[11] "test_config_5_no_dummies_spec <- "
[12] " mars() %>% "
[13] " set_mode(\"classification\") %>% "
[14] " set_engine(\"earth\") "
[15] ""
[16] "test_config_5_no_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_5_no_dummies_recipe) %>% "
[19] " add_model(test_config_5_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_6_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Regularization methods sum up functions of the model slope "
[10] " ## coefficients. Because of this, the predictor variables should be on "
[11] " ## the same scale. Before centering and scaling the numeric predictors, "
[12] " ## any predictors with a single unique value are filtered out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_6_dummies_spec <- "
[17] " linear_reg() %>% "
[18] " set_mode(\"regression\") %>% "
[19] " set_engine(\"glmnet\") "
[20] ""
[21] "test_config_6_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_6_dummies_recipe) %>% "
[24] " add_model(test_config_6_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_6_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Regularization methods sum up functions of the model slope "
[10] " ## coefficients. Because of this, the predictor variables should be on "
[11] " ## the same scale. Before centering and scaling the numeric predictors, "
[12] " ## any predictors with a single unique value are filtered out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_6_no_dummies_spec <- "
[17] " multinom_reg() %>% "
[18] " set_mode(\"classification\") %>% "
[19] " set_engine(\"glmnet\") "
[20] ""
[21] "test_config_6_no_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_6_no_dummies_recipe) %>% "
[24] " add_model(test_config_6_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_7_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_7_dummies_spec <- "
[11] " svm_poly() %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_7_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_7_dummies_recipe) %>% "
[17] " add_model(test_config_7_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_7_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_7_no_dummies_spec <- "
[11] " svm_poly() %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_7_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_7_no_dummies_recipe) %>% "
[17] " add_model(test_config_7_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_8_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_8_dummies_spec <- "
[11] " svm_rbf() %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_8_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_8_dummies_recipe) %>% "
[17] " add_model(test_config_8_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_8_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_8_no_dummies_spec <- "
[11] " svm_rbf() %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_8_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_8_no_dummies_recipe) %>% "
[17] " add_model(test_config_8_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_9_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Since distance calculations are used, the predictor variables should "
[10] " ## be on the same scale. Before centering and scaling the numeric "
[11] " ## predictors, any predictors with a single unique value are filtered "
[12] " ## out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_9_dummies_spec <- "
[17] " nearest_neighbor() %>% "
[18] " set_mode(\"regression\") %>% "
[19] " set_engine(\"kknn\") "
[20] ""
[21] "test_config_9_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_9_dummies_recipe) %>% "
[24] " add_model(test_config_9_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_9_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Since distance calculations are used, the predictor variables should "
[10] " ## be on the same scale. Before centering and scaling the numeric "
[11] " ## predictors, any predictors with a single unique value are filtered "
[12] " ## out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_9_no_dummies_spec <- "
[17] " nearest_neighbor() %>% "
[18] " set_mode(\"classification\") %>% "
[19] " set_engine(\"kknn\") "
[20] ""
[21] "test_config_9_no_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_9_no_dummies_recipe) %>% "
[24] " add_model(test_config_9_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_10_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_10_dummies_spec <- "
[5] " gen_additive_mod() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_10_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_10_dummies_recipe) %>% "
[12] " add_model(test_config_10_dummies_spec, formula = stop(\"add your gam formula\")) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_10_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_10_no_dummies_spec <- "
[5] " gen_additive_mod() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_10_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_10_no_dummies_recipe) %>% "
[12] " add_model(test_config_10_no_dummies_spec, formula = stop(\"add your gam formula\")) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_11_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_11_dummies_spec <- "
[15] " pls() %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"mixOmics\") "
[18] ""
[19] "test_config_11_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_11_dummies_recipe) %>% "
[22] " add_model(test_config_11_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_11_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_11_no_dummies_spec <- "
[15] " pls() %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"mixOmics\") "
[18] ""
[19] "test_config_11_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_11_no_dummies_recipe) %>% "
[22] " add_model(test_config_11_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_12_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) %>% "
[10] " step_normalize(all_numeric_predictors()) "
[11] ""
[12] "test_config_12_dummies_spec <- "
[13] " mlp() %>% "
[14] " set_mode(\"regression\") "
[15] ""
[16] "test_config_12_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_12_dummies_recipe) %>% "
[19] " add_model(test_config_12_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_12_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) %>% "
[10] " step_normalize(all_numeric_predictors()) "
[11] ""
[12] "test_config_12_no_dummies_spec <- "
[13] " mlp() %>% "
[14] " set_mode(\"classification\") "
[15] ""
[16] "test_config_12_no_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_12_no_dummies_recipe) %>% "
[19] " add_model(test_config_12_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_13_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_13_dummies_spec <- "
[5] " rand_forest(trees = 1000) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_13_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_13_dummies_recipe) %>% "
[12] " add_model(test_config_13_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_13_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_13_no_dummies_spec <- "
[5] " rand_forest(trees = 1000) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_13_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_13_no_dummies_recipe) %>% "
[12] " add_model(test_config_13_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_14_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_14_dummies_spec <- "
[5] " decision_tree() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_14_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_14_dummies_recipe) %>% "
[12] " add_model(test_config_14_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_14_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_14_no_dummies_spec <- "
[5] " decision_tree() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_14_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_14_no_dummies_recipe) %>% "
[12] " add_model(test_config_14_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_15_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. However, for this model, binary indicator variables can be "
[8] " ## made for each of the levels of the factors (known as 'one-hot "
[9] " ## encoding'). "
[10] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[11] " step_zv(all_predictors()) "
[12] ""
[13] "test_config_15_dummies_spec <- "
[14] " boost_tree() %>% "
[15] " set_mode(\"regression\") %>% "
[16] " set_engine(\"xgboost\") "
[17] ""
[18] "test_config_15_dummies_workflow <- "
[19] " workflow() %>% "
[20] " add_recipe(test_config_15_dummies_recipe) %>% "
[21] " add_model(test_config_15_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_15_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. However, for this model, binary indicator variables can be "
[8] " ## made for each of the levels of the factors (known as 'one-hot "
[9] " ## encoding'). "
[10] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[11] " step_zv(all_predictors()) "
[12] ""
[13] "test_config_15_no_dummies_spec <- "
[14] " boost_tree() %>% "
[15] " set_mode(\"classification\") %>% "
[16] " set_engine(\"xgboost\") "
[17] ""
[18] "test_config_15_no_dummies_workflow <- "
[19] " workflow() %>% "
[20] " add_recipe(test_config_15_no_dummies_recipe) %>% "
[21] " add_model(test_config_15_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_16_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_16_dummies_spec <- "
[15] " rule_fit() %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"xrf\") "
[18] ""
[19] "test_config_16_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_16_dummies_recipe) %>% "
[22] " add_model(test_config_16_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_16_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_16_no_dummies_spec <- "
[15] " rule_fit() %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"xrf\") "
[18] ""
[19] "test_config_16_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_16_no_dummies_recipe) %>% "
[22] " add_model(test_config_16_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_17_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) "
[5] ""
[6] "test_config_17_dummies_spec <- "
[7] " bag_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[8] " set_mode(\"regression\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_17_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_17_dummies_recipe) %>% "
[14] " add_model(test_config_17_dummies_spec) "
[15] ""
[16] "set.seed(27246)"
[17] "test_config_17_dummies_tune <-"
[18] " tune_grid(test_config_17_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[19] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_17_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) "
[5] ""
[6] "test_config_17_no_dummies_spec <- "
[7] " bag_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[8] " set_mode(\"classification\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_17_no_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_17_no_dummies_recipe) %>% "
[14] " add_model(test_config_17_no_dummies_spec) "
[15] ""
[16] "set.seed(27246)"
[17] "test_config_17_no_dummies_tune <-"
[18] " tune_grid(test_config_17_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[19] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_18_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_18_no_dummies_spec <- "
[5] " boost_tree(trees = tune(), min_n = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"C5.0\") "
[8] ""
[9] "test_config_18_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_18_no_dummies_recipe) %>% "
[12] " add_model(test_config_18_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_18_no_dummies_tune <-"
[16] " tune_grid(test_config_18_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_19_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_19_dummies_spec <- "
[8] " cubist_rules(committees = tune(), neighbors = tune()) %>% "
[9] " set_engine(\"Cubist\") "
[10] ""
[11] "test_config_19_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_19_dummies_recipe) %>% "
[14] " add_model(test_config_19_dummies_spec) "
[15] ""
[16] "test_config_19_dummies_grid <- tidyr::crossing(committees = c(1:9, (1:5) * "
[17] " 10), neighbors = c(0, 3, 6, 9)) "
[18] ""
[19] "test_config_19_dummies_tune <- "
[20] " tune_grid(test_config_19_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = test_config_19_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_20_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_20_dummies_spec <- "
[5] " bart(trees = tune(), prior_terminal_node_coef = tune(), prior_terminal_node_expo = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_20_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_20_dummies_recipe) %>% "
[12] " add_model(test_config_20_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_20_dummies_tune <-"
[16] " tune_grid(test_config_20_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_20_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_20_no_dummies_spec <- "
[5] " bart(trees = tune(), prior_terminal_node_coef = tune(), prior_terminal_node_expo = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_20_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_20_no_dummies_recipe) %>% "
[12] " add_model(test_config_20_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_20_no_dummies_tune <-"
[16] " tune_grid(test_config_20_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Output
## MARS models can make predictions on many _sub_models_, meaning that we
## can evaluate many values of `num_terms` without much computational
## cost. A regular grid is used to exploit this property. The first term
## is only the intercept, so the grid is a sequence of even numbered
## values.
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_21_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) "
[10] ""
[11] "test_config_21_dummies_spec <- "
[12] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[13] " set_mode(\"regression\") %>% "
[14] " set_engine(\"earth\") "
[15] ""
[16] "test_config_21_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_21_dummies_recipe) %>% "
[19] " add_model(test_config_21_dummies_spec) "
[20] ""
[21] "test_config_21_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[22] ""
[23] "test_config_21_dummies_tune <- "
[24] " tune_grid(test_config_21_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[25] " grid = test_config_21_dummies_grid) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Output
## MARS models can make predictions on many _sub_models_, meaning that we
## can evaluate many values of `num_terms` without much computational
## cost. A regular grid is used to exploit this property. The first term
## is only the intercept, so the grid is a sequence of even numbered
## values.
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_21_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) "
[10] ""
[11] "test_config_21_no_dummies_spec <- "
[12] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[13] " set_mode(\"classification\") %>% "
[14] " set_engine(\"earth\") "
[15] ""
[16] "test_config_21_no_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_21_no_dummies_recipe) %>% "
[19] " add_model(test_config_21_no_dummies_spec) "
[20] ""
[21] "test_config_21_no_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[22] ""
[23] "test_config_21_no_dummies_tune <- "
[24] " tune_grid(test_config_21_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[25] " grid = test_config_21_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_22_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Regularization methods sum up functions of the model slope "
[10] " ## coefficients. Because of this, the predictor variables should be on "
[11] " ## the same scale. Before centering and scaling the numeric predictors, "
[12] " ## any predictors with a single unique value are filtered out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_22_dummies_spec <- "
[17] " linear_reg(penalty = tune(), mixture = tune()) %>% "
[18] " set_mode(\"regression\") %>% "
[19] " set_engine(\"glmnet\") "
[20] ""
[21] "test_config_22_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_22_dummies_recipe) %>% "
[24] " add_model(test_config_22_dummies_spec) "
[25] ""
[26] "test_config_22_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20), "
[27] " mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[28] ""
[29] "test_config_22_dummies_tune <- "
[30] " tune_grid(test_config_22_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[31] " grid = test_config_22_dummies_grid) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_22_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Regularization methods sum up functions of the model slope "
[10] " ## coefficients. Because of this, the predictor variables should be on "
[11] " ## the same scale. Before centering and scaling the numeric predictors, "
[12] " ## any predictors with a single unique value are filtered out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_22_no_dummies_spec <- "
[17] " multinom_reg(penalty = tune(), mixture = tune()) %>% "
[18] " set_mode(\"classification\") %>% "
[19] " set_engine(\"glmnet\") "
[20] ""
[21] "test_config_22_no_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_22_no_dummies_recipe) %>% "
[24] " add_model(test_config_22_no_dummies_spec) "
[25] ""
[26] "test_config_22_no_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, "
[27] " length.out = 20), mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[28] ""
[29] "test_config_22_no_dummies_tune <- "
[30] " tune_grid(test_config_22_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[31] " grid = test_config_22_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_23_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_23_dummies_spec <- "
[11] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_23_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_23_dummies_recipe) %>% "
[17] " add_model(test_config_23_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_23_dummies_tune <-"
[21] " tune_grid(test_config_23_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_23_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_23_no_dummies_spec <- "
[11] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_23_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_23_no_dummies_recipe) %>% "
[17] " add_model(test_config_23_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_23_no_dummies_tune <-"
[21] " tune_grid(test_config_23_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_24_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_24_dummies_spec <- "
[11] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_24_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_24_dummies_recipe) %>% "
[17] " add_model(test_config_24_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_24_dummies_tune <-"
[21] " tune_grid(test_config_24_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_24_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_24_no_dummies_spec <- "
[11] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_24_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_24_no_dummies_recipe) %>% "
[17] " add_model(test_config_24_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_24_no_dummies_tune <-"
[21] " tune_grid(test_config_24_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_25_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Since distance calculations are used, the predictor variables should "
[10] " ## be on the same scale. Before centering and scaling the numeric "
[11] " ## predictors, any predictors with a single unique value are filtered "
[12] " ## out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_25_dummies_spec <- "
[17] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[18] " set_mode(\"regression\") %>% "
[19] " set_engine(\"kknn\") "
[20] ""
[21] "test_config_25_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_25_dummies_recipe) %>% "
[24] " add_model(test_config_25_dummies_spec) "
[25] ""
[26] "set.seed(27246)"
[27] "test_config_25_dummies_tune <-"
[28] " tune_grid(test_config_25_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[29] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_25_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Since distance calculations are used, the predictor variables should "
[10] " ## be on the same scale. Before centering and scaling the numeric "
[11] " ## predictors, any predictors with a single unique value are filtered "
[12] " ## out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_25_no_dummies_spec <- "
[17] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[18] " set_mode(\"classification\") %>% "
[19] " set_engine(\"kknn\") "
[20] ""
[21] "test_config_25_no_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_25_no_dummies_recipe) %>% "
[24] " add_model(test_config_25_no_dummies_spec) "
[25] ""
[26] "set.seed(27246)"
[27] "test_config_25_no_dummies_tune <-"
[28] " tune_grid(test_config_25_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[29] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_26_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_26_dummies_spec <- "
[5] " gen_additive_mod(select_features = tune(), adjust_deg_free = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_26_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_26_dummies_recipe) %>% "
[12] " add_model(test_config_26_dummies_spec, formula = stop(\"add your gam formula\")) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_26_dummies_tune <-"
[16] " tune_grid(test_config_26_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_26_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_26_no_dummies_spec <- "
[5] " gen_additive_mod(select_features = tune(), adjust_deg_free = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_26_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_26_no_dummies_recipe) %>% "
[12] " add_model(test_config_26_no_dummies_spec, formula = stop(\"add your gam formula\")) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_26_no_dummies_tune <-"
[16] " tune_grid(test_config_26_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_27_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_27_dummies_spec <- "
[15] " pls(predictor_prop = tune(), num_comp = tune()) %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"mixOmics\") "
[18] ""
[19] "test_config_27_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_27_dummies_recipe) %>% "
[22] " add_model(test_config_27_dummies_spec) "
[23] ""
[24] "set.seed(27246)"
[25] "test_config_27_dummies_tune <-"
[26] " tune_grid(test_config_27_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[27] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_27_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_27_no_dummies_spec <- "
[15] " pls(predictor_prop = tune(), num_comp = tune()) %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"mixOmics\") "
[18] ""
[19] "test_config_27_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_27_no_dummies_recipe) %>% "
[22] " add_model(test_config_27_no_dummies_spec) "
[23] ""
[24] "set.seed(27246)"
[25] "test_config_27_no_dummies_tune <-"
[26] " tune_grid(test_config_27_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[27] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_28_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) %>% "
[10] " step_normalize(all_numeric_predictors()) "
[11] ""
[12] "test_config_28_dummies_spec <- "
[13] " mlp(hidden_units = tune(), penalty = tune(), epochs = tune()) %>% "
[14] " set_mode(\"regression\") "
[15] ""
[16] "test_config_28_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_28_dummies_recipe) %>% "
[19] " add_model(test_config_28_dummies_spec) "
[20] ""
[21] "set.seed(27246)"
[22] "test_config_28_dummies_tune <-"
[23] " tune_grid(test_config_28_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_28_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) %>% "
[10] " step_normalize(all_numeric_predictors()) "
[11] ""
[12] "test_config_28_no_dummies_spec <- "
[13] " mlp(hidden_units = tune(), penalty = tune(), epochs = tune()) %>% "
[14] " set_mode(\"classification\") "
[15] ""
[16] "test_config_28_no_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_28_no_dummies_recipe) %>% "
[19] " add_model(test_config_28_no_dummies_spec) "
[20] ""
[21] "set.seed(27246)"
[22] "test_config_28_no_dummies_tune <-"
[23] " tune_grid(test_config_28_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_29_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_29_dummies_spec <- "
[5] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_29_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_29_dummies_recipe) %>% "
[12] " add_model(test_config_29_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_29_dummies_tune <-"
[16] " tune_grid(test_config_29_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_29_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_29_no_dummies_spec <- "
[5] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_29_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_29_no_dummies_recipe) %>% "
[12] " add_model(test_config_29_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_29_no_dummies_tune <-"
[16] " tune_grid(test_config_29_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_30_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_30_dummies_spec <- "
[5] " decision_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_30_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_30_dummies_recipe) %>% "
[12] " add_model(test_config_30_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_30_dummies_tune <-"
[16] " tune_grid(test_config_30_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_30_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_30_no_dummies_spec <- "
[5] " decision_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_30_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_30_no_dummies_recipe) %>% "
[12] " add_model(test_config_30_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_30_no_dummies_tune <-"
[16] " tune_grid(test_config_30_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_31_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. However, for this model, binary indicator variables can be "
[8] " ## made for each of the levels of the factors (known as 'one-hot "
[9] " ## encoding'). "
[10] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[11] " step_zv(all_predictors()) "
[12] ""
[13] "test_config_31_dummies_spec <- "
[14] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[15] " loss_reduction = tune(), sample_size = tune()) %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"xgboost\") "
[18] ""
[19] "test_config_31_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_31_dummies_recipe) %>% "
[22] " add_model(test_config_31_dummies_spec) "
[23] ""
[24] "set.seed(27246)"
[25] "test_config_31_dummies_tune <-"
[26] " tune_grid(test_config_31_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[27] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_31_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. However, for this model, binary indicator variables can be "
[8] " ## made for each of the levels of the factors (known as 'one-hot "
[9] " ## encoding'). "
[10] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[11] " step_zv(all_predictors()) "
[12] ""
[13] "test_config_31_no_dummies_spec <- "
[14] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[15] " loss_reduction = tune(), sample_size = tune()) %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"xgboost\") "
[18] ""
[19] "test_config_31_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_31_no_dummies_recipe) %>% "
[22] " add_model(test_config_31_no_dummies_spec) "
[23] ""
[24] "set.seed(27246)"
[25] "test_config_31_no_dummies_tune <-"
[26] " tune_grid(test_config_31_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[27] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_32_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_32_dummies_spec <- "
[15] " rule_fit(mtry = tune(), trees = tune(), min_n = tune(), tree_depth = tune(), "
[16] " learn_rate = tune(), loss_reduction = tune(), sample_size = tune(), penalty = tune()) %>% "
[17] " set_mode(\"regression\") %>% "
[18] " set_engine(\"xrf\") "
[19] ""
[20] "test_config_32_dummies_workflow <- "
[21] " workflow() %>% "
[22] " add_recipe(test_config_32_dummies_recipe) %>% "
[23] " add_model(test_config_32_dummies_spec) "
[24] ""
[25] "set.seed(27246)"
[26] "test_config_32_dummies_tune <-"
[27] " tune_grid(test_config_32_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[28] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_32_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_32_no_dummies_spec <- "
[15] " rule_fit(mtry = tune(), trees = tune(), min_n = tune(), tree_depth = tune(), "
[16] " learn_rate = tune(), loss_reduction = tune(), sample_size = tune(), penalty = tune()) %>% "
[17] " set_mode(\"classification\") %>% "
[18] " set_engine(\"xrf\") "
[19] ""
[20] "test_config_32_no_dummies_workflow <- "
[21] " workflow() %>% "
[22] " add_recipe(test_config_32_no_dummies_recipe) %>% "
[23] " add_model(test_config_32_no_dummies_spec) "
[24] ""
[25] "set.seed(27246)"
[26] "test_config_32_no_dummies_tune <-"
[27] " tune_grid(test_config_32_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[28] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_33_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) "
[5] ""
[6] "test_config_33_dummies_spec <- "
[7] " bag_tree() %>% "
[8] " set_mode(\"regression\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_33_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_33_dummies_recipe) %>% "
[14] " add_model(test_config_33_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_33_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) "
[5] ""
[6] "test_config_33_no_dummies_spec <- "
[7] " bag_tree() %>% "
[8] " set_mode(\"classification\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_33_no_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_33_no_dummies_recipe) %>% "
[14] " add_model(test_config_33_no_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_34_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_34_no_dummies_spec <- "
[5] " boost_tree() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"C5.0\") "
[8] ""
[9] "test_config_34_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_34_no_dummies_recipe) %>% "
[12] " add_model(test_config_34_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_35_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_35_dummies_spec <- "
[8] " cubist_rules() %>% "
[9] " set_engine(\"Cubist\") "
[10] ""
[11] "test_config_35_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_35_dummies_recipe) %>% "
[14] " add_model(test_config_35_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_36_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_36_dummies_spec <- "
[5] " bart() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_36_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_36_dummies_recipe) %>% "
[12] " add_model(test_config_36_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_36_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_36_no_dummies_spec <- "
[5] " bart() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_36_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_36_no_dummies_recipe) %>% "
[12] " add_model(test_config_36_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_37_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_37_dummies_spec <- "
[8] " mars() %>% "
[9] " set_mode(\"regression\") %>% "
[10] " set_engine(\"earth\") "
[11] ""
[12] "test_config_37_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_37_dummies_recipe) %>% "
[15] " add_model(test_config_37_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_37_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_37_no_dummies_spec <- "
[8] " mars() %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"earth\") "
[11] ""
[12] "test_config_37_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_37_no_dummies_recipe) %>% "
[15] " add_model(test_config_37_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_38_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_38_dummies_spec <- "
[9] " linear_reg() %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"glmnet\") "
[12] ""
[13] "test_config_38_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_38_dummies_recipe) %>% "
[16] " add_model(test_config_38_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_38_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_38_no_dummies_spec <- "
[9] " multinom_reg() %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"glmnet\") "
[12] ""
[13] "test_config_38_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_38_no_dummies_recipe) %>% "
[16] " add_model(test_config_38_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_39_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_39_dummies_spec <- "
[7] " svm_poly() %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_39_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_39_dummies_recipe) %>% "
[13] " add_model(test_config_39_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_39_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_39_no_dummies_spec <- "
[7] " svm_poly() %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_39_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_39_no_dummies_recipe) %>% "
[13] " add_model(test_config_39_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_40_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_40_dummies_spec <- "
[7] " svm_rbf() %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_40_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_40_dummies_recipe) %>% "
[13] " add_model(test_config_40_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_40_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_40_no_dummies_spec <- "
[7] " svm_rbf() %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_40_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_40_no_dummies_recipe) %>% "
[13] " add_model(test_config_40_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_41_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_41_dummies_spec <- "
[9] " nearest_neighbor() %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"kknn\") "
[12] ""
[13] "test_config_41_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_41_dummies_recipe) %>% "
[16] " add_model(test_config_41_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_41_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_41_no_dummies_spec <- "
[9] " nearest_neighbor() %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"kknn\") "
[12] ""
[13] "test_config_41_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_41_no_dummies_recipe) %>% "
[16] " add_model(test_config_41_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_42_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_42_dummies_spec <- "
[5] " gen_additive_mod() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_42_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_42_dummies_recipe) %>% "
[12] " add_model(test_config_42_dummies_spec, formula = stop(\"add your gam formula\")) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_42_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_42_no_dummies_spec <- "
[5] " gen_additive_mod() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_42_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_42_no_dummies_recipe) %>% "
[12] " add_model(test_config_42_no_dummies_spec, formula = stop(\"add your gam formula\")) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_43_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_43_dummies_spec <- "
[11] " pls() %>% "
[12] " set_mode(\"regression\") %>% "
[13] " set_engine(\"mixOmics\") "
[14] ""
[15] "test_config_43_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_43_dummies_recipe) %>% "
[18] " add_model(test_config_43_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_43_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_43_no_dummies_spec <- "
[11] " pls() %>% "
[12] " set_mode(\"classification\") %>% "
[13] " set_engine(\"mixOmics\") "
[14] ""
[15] "test_config_43_no_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_43_no_dummies_recipe) %>% "
[18] " add_model(test_config_43_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_44_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_44_dummies_spec <- "
[9] " mlp() %>% "
[10] " set_mode(\"regression\") "
[11] ""
[12] "test_config_44_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_44_dummies_recipe) %>% "
[15] " add_model(test_config_44_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_44_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_44_no_dummies_spec <- "
[9] " mlp() %>% "
[10] " set_mode(\"classification\") "
[11] ""
[12] "test_config_44_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_44_no_dummies_recipe) %>% "
[15] " add_model(test_config_44_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_45_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_45_dummies_spec <- "
[5] " rand_forest(trees = 1000) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_45_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_45_dummies_recipe) %>% "
[12] " add_model(test_config_45_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_45_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_45_no_dummies_spec <- "
[5] " rand_forest(trees = 1000) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_45_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_45_no_dummies_recipe) %>% "
[12] " add_model(test_config_45_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_46_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_46_dummies_spec <- "
[5] " decision_tree() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_46_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_46_dummies_recipe) %>% "
[12] " add_model(test_config_46_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_46_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_46_no_dummies_spec <- "
[5] " decision_tree() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_46_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_46_no_dummies_recipe) %>% "
[12] " add_model(test_config_46_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_47_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_47_dummies_spec <- "
[8] " boost_tree() %>% "
[9] " set_mode(\"regression\") %>% "
[10] " set_engine(\"xgboost\") "
[11] ""
[12] "test_config_47_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_47_dummies_recipe) %>% "
[15] " add_model(test_config_47_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_47_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_47_no_dummies_spec <- "
[8] " boost_tree() %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"xgboost\") "
[11] ""
[12] "test_config_47_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_47_no_dummies_recipe) %>% "
[15] " add_model(test_config_47_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_48_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_48_dummies_spec <- "
[11] " rule_fit() %>% "
[12] " set_mode(\"regression\") %>% "
[13] " set_engine(\"xrf\") "
[14] ""
[15] "test_config_48_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_48_dummies_recipe) %>% "
[18] " add_model(test_config_48_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_48_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_48_no_dummies_spec <- "
[11] " rule_fit() %>% "
[12] " set_mode(\"classification\") %>% "
[13] " set_engine(\"xrf\") "
[14] ""
[15] "test_config_48_no_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_48_no_dummies_recipe) %>% "
[18] " add_model(test_config_48_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_49_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) "
[5] ""
[6] "test_config_49_dummies_spec <- "
[7] " bag_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[8] " set_mode(\"regression\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_49_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_49_dummies_recipe) %>% "
[14] " add_model(test_config_49_dummies_spec) "
[15] ""
[16] "set.seed(27246)"
[17] "test_config_49_dummies_tune <-"
[18] " tune_grid(test_config_49_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[19] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_49_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) "
[5] ""
[6] "test_config_49_no_dummies_spec <- "
[7] " bag_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[8] " set_mode(\"classification\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_49_no_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_49_no_dummies_recipe) %>% "
[14] " add_model(test_config_49_no_dummies_spec) "
[15] ""
[16] "set.seed(27246)"
[17] "test_config_49_no_dummies_tune <-"
[18] " tune_grid(test_config_49_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[19] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_50_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_50_no_dummies_spec <- "
[5] " boost_tree(trees = tune(), min_n = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"C5.0\") "
[8] ""
[9] "test_config_50_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_50_no_dummies_recipe) %>% "
[12] " add_model(test_config_50_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_50_no_dummies_tune <-"
[16] " tune_grid(test_config_50_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_51_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_51_dummies_spec <- "
[8] " cubist_rules(committees = tune(), neighbors = tune()) %>% "
[9] " set_engine(\"Cubist\") "
[10] ""
[11] "test_config_51_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_51_dummies_recipe) %>% "
[14] " add_model(test_config_51_dummies_spec) "
[15] ""
[16] "test_config_51_dummies_grid <- tidyr::crossing(committees = c(1:9, (1:5) * "
[17] " 10), neighbors = c(0, 3, 6, 9)) "
[18] ""
[19] "test_config_51_dummies_tune <- "
[20] " tune_grid(test_config_51_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = test_config_51_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_52_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_52_dummies_spec <- "
[5] " bart(trees = tune(), prior_terminal_node_coef = tune(), prior_terminal_node_expo = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_52_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_52_dummies_recipe) %>% "
[12] " add_model(test_config_52_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_52_dummies_tune <-"
[16] " tune_grid(test_config_52_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_52_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_52_no_dummies_spec <- "
[5] " bart(trees = tune(), prior_terminal_node_coef = tune(), prior_terminal_node_expo = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_52_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_52_no_dummies_recipe) %>% "
[12] " add_model(test_config_52_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_52_no_dummies_tune <-"
[16] " tune_grid(test_config_52_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_53_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_53_dummies_spec <- "
[8] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[9] " set_mode(\"regression\") %>% "
[10] " set_engine(\"earth\") "
[11] ""
[12] "test_config_53_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_53_dummies_recipe) %>% "
[15] " add_model(test_config_53_dummies_spec) "
[16] ""
[17] "test_config_53_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[18] ""
[19] "test_config_53_dummies_tune <- "
[20] " tune_grid(test_config_53_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = test_config_53_dummies_grid) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_53_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_53_no_dummies_spec <- "
[8] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"earth\") "
[11] ""
[12] "test_config_53_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_53_no_dummies_recipe) %>% "
[15] " add_model(test_config_53_no_dummies_spec) "
[16] ""
[17] "test_config_53_no_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[18] ""
[19] "test_config_53_no_dummies_tune <- "
[20] " tune_grid(test_config_53_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = test_config_53_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_54_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_54_dummies_spec <- "
[9] " linear_reg(penalty = tune(), mixture = tune()) %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"glmnet\") "
[12] ""
[13] "test_config_54_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_54_dummies_recipe) %>% "
[16] " add_model(test_config_54_dummies_spec) "
[17] ""
[18] "test_config_54_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20), "
[19] " mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[20] ""
[21] "test_config_54_dummies_tune <- "
[22] " tune_grid(test_config_54_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[23] " grid = test_config_54_dummies_grid) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_54_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_54_no_dummies_spec <- "
[9] " multinom_reg(penalty = tune(), mixture = tune()) %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"glmnet\") "
[12] ""
[13] "test_config_54_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_54_no_dummies_recipe) %>% "
[16] " add_model(test_config_54_no_dummies_spec) "
[17] ""
[18] "test_config_54_no_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, "
[19] " length.out = 20), mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[20] ""
[21] "test_config_54_no_dummies_tune <- "
[22] " tune_grid(test_config_54_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[23] " grid = test_config_54_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_55_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_55_dummies_spec <- "
[7] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_55_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_55_dummies_recipe) %>% "
[13] " add_model(test_config_55_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_55_dummies_tune <-"
[17] " tune_grid(test_config_55_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[18] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_55_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_55_no_dummies_spec <- "
[7] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_55_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_55_no_dummies_recipe) %>% "
[13] " add_model(test_config_55_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_55_no_dummies_tune <-"
[17] " tune_grid(test_config_55_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[18] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_56_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_56_dummies_spec <- "
[7] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_56_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_56_dummies_recipe) %>% "
[13] " add_model(test_config_56_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_56_dummies_tune <-"
[17] " tune_grid(test_config_56_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[18] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_56_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_56_no_dummies_spec <- "
[7] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_56_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_56_no_dummies_recipe) %>% "
[13] " add_model(test_config_56_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_56_no_dummies_tune <-"
[17] " tune_grid(test_config_56_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[18] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_57_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_57_dummies_spec <- "
[9] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"kknn\") "
[12] ""
[13] "test_config_57_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_57_dummies_recipe) %>% "
[16] " add_model(test_config_57_dummies_spec) "
[17] ""
[18] "set.seed(27246)"
[19] "test_config_57_dummies_tune <-"
[20] " tune_grid(test_config_57_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_57_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_57_no_dummies_spec <- "
[9] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"kknn\") "
[12] ""
[13] "test_config_57_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_57_no_dummies_recipe) %>% "
[16] " add_model(test_config_57_no_dummies_spec) "
[17] ""
[18] "set.seed(27246)"
[19] "test_config_57_no_dummies_tune <-"
[20] " tune_grid(test_config_57_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_58_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_58_dummies_spec <- "
[5] " gen_additive_mod(select_features = tune(), adjust_deg_free = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_58_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_58_dummies_recipe) %>% "
[12] " add_model(test_config_58_dummies_spec, formula = stop(\"add your gam formula\")) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_58_dummies_tune <-"
[16] " tune_grid(test_config_58_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_58_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_58_no_dummies_spec <- "
[5] " gen_additive_mod(select_features = tune(), adjust_deg_free = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_58_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_58_no_dummies_recipe) %>% "
[12] " add_model(test_config_58_no_dummies_spec, formula = stop(\"add your gam formula\")) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_58_no_dummies_tune <-"
[16] " tune_grid(test_config_58_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_59_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_59_dummies_spec <- "
[11] " pls(predictor_prop = tune(), num_comp = tune()) %>% "
[12] " set_mode(\"regression\") %>% "
[13] " set_engine(\"mixOmics\") "
[14] ""
[15] "test_config_59_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_59_dummies_recipe) %>% "
[18] " add_model(test_config_59_dummies_spec) "
[19] ""
[20] "set.seed(27246)"
[21] "test_config_59_dummies_tune <-"
[22] " tune_grid(test_config_59_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[23] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_59_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_59_no_dummies_spec <- "
[11] " pls(predictor_prop = tune(), num_comp = tune()) %>% "
[12] " set_mode(\"classification\") %>% "
[13] " set_engine(\"mixOmics\") "
[14] ""
[15] "test_config_59_no_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_59_no_dummies_recipe) %>% "
[18] " add_model(test_config_59_no_dummies_spec) "
[19] ""
[20] "set.seed(27246)"
[21] "test_config_59_no_dummies_tune <-"
[22] " tune_grid(test_config_59_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[23] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_60_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_60_dummies_spec <- "
[9] " mlp(hidden_units = tune(), penalty = tune(), epochs = tune()) %>% "
[10] " set_mode(\"regression\") "
[11] ""
[12] "test_config_60_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_60_dummies_recipe) %>% "
[15] " add_model(test_config_60_dummies_spec) "
[16] ""
[17] "set.seed(27246)"
[18] "test_config_60_dummies_tune <-"
[19] " tune_grid(test_config_60_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[20] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_60_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_60_no_dummies_spec <- "
[9] " mlp(hidden_units = tune(), penalty = tune(), epochs = tune()) %>% "
[10] " set_mode(\"classification\") "
[11] ""
[12] "test_config_60_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_60_no_dummies_recipe) %>% "
[15] " add_model(test_config_60_no_dummies_spec) "
[16] ""
[17] "set.seed(27246)"
[18] "test_config_60_no_dummies_tune <-"
[19] " tune_grid(test_config_60_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[20] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_61_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_61_dummies_spec <- "
[5] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_61_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_61_dummies_recipe) %>% "
[12] " add_model(test_config_61_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_61_dummies_tune <-"
[16] " tune_grid(test_config_61_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_61_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_61_no_dummies_spec <- "
[5] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_61_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_61_no_dummies_recipe) %>% "
[12] " add_model(test_config_61_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_61_no_dummies_tune <-"
[16] " tune_grid(test_config_61_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_62_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_62_dummies_spec <- "
[5] " decision_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_62_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_62_dummies_recipe) %>% "
[12] " add_model(test_config_62_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_62_dummies_tune <-"
[16] " tune_grid(test_config_62_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_62_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_62_no_dummies_spec <- "
[5] " decision_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_62_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_62_no_dummies_recipe) %>% "
[12] " add_model(test_config_62_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_62_no_dummies_tune <-"
[16] " tune_grid(test_config_62_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_63_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_63_dummies_spec <- "
[8] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[9] " loss_reduction = tune(), sample_size = tune()) %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"xgboost\") "
[12] ""
[13] "test_config_63_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_63_dummies_recipe) %>% "
[16] " add_model(test_config_63_dummies_spec) "
[17] ""
[18] "set.seed(27246)"
[19] "test_config_63_dummies_tune <-"
[20] " tune_grid(test_config_63_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_63_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_63_no_dummies_spec <- "
[8] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[9] " loss_reduction = tune(), sample_size = tune()) %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"xgboost\") "
[12] ""
[13] "test_config_63_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_63_no_dummies_recipe) %>% "
[16] " add_model(test_config_63_no_dummies_spec) "
[17] ""
[18] "set.seed(27246)"
[19] "test_config_63_no_dummies_tune <-"
[20] " tune_grid(test_config_63_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_64_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_64_dummies_spec <- "
[11] " rule_fit(mtry = tune(), trees = tune(), min_n = tune(), tree_depth = tune(), "
[12] " learn_rate = tune(), loss_reduction = tune(), sample_size = tune(), penalty = tune()) %>% "
[13] " set_mode(\"regression\") %>% "
[14] " set_engine(\"xrf\") "
[15] ""
[16] "test_config_64_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_64_dummies_recipe) %>% "
[19] " add_model(test_config_64_dummies_spec) "
[20] ""
[21] "set.seed(27246)"
[22] "test_config_64_dummies_tune <-"
[23] " tune_grid(test_config_64_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_64_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_64_no_dummies_spec <- "
[11] " rule_fit(mtry = tune(), trees = tune(), min_n = tune(), tree_depth = tune(), "
[12] " learn_rate = tune(), loss_reduction = tune(), sample_size = tune(), penalty = tune()) %>% "
[13] " set_mode(\"classification\") %>% "
[14] " set_engine(\"xrf\") "
[15] ""
[16] "test_config_64_no_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_64_no_dummies_recipe) %>% "
[19] " add_model(test_config_64_no_dummies_spec) "
[20] ""
[21] "set.seed(27246)"
[22] "test_config_64_no_dummies_tune <-"
[23] " tune_grid(test_config_64_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = stop(\"add number of candidate points\"))"
tidymodels/usemodels documentation built on March 27, 2023, 1:21 a.m.
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all model templates with clipboard
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_1_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) "
[5] ""
[6] "test_config_1_dummies_spec <- "
[7] " bag_tree() %>% "
[8] " set_mode(\"regression\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_1_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_1_dummies_recipe) %>% "
[14] " add_model(test_config_1_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_1_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) "
[5] ""
[6] "test_config_1_no_dummies_spec <- "
[7] " bag_tree() %>% "
[8] " set_mode(\"classification\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_1_no_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_1_no_dummies_recipe) %>% "
[14] " add_model(test_config_1_no_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_2_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_2_no_dummies_spec <- "
[5] " boost_tree() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"C5.0\") "
[8] ""
[9] "test_config_2_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_2_no_dummies_recipe) %>% "
[12] " add_model(test_config_2_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_3_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_3_dummies_spec <- "
[8] " cubist_rules() %>% "
[9] " set_engine(\"Cubist\") "
[10] ""
[11] "test_config_3_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_3_dummies_recipe) %>% "
[14] " add_model(test_config_3_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_4_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_4_dummies_spec <- "
[5] " bart() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_4_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_4_dummies_recipe) %>% "
[12] " add_model(test_config_4_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_4_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_4_no_dummies_spec <- "
[5] " bart() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_4_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_4_no_dummies_recipe) %>% "
[12] " add_model(test_config_4_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_5_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) "
[10] ""
[11] "test_config_5_dummies_spec <- "
[12] " mars() %>% "
[13] " set_mode(\"regression\") %>% "
[14] " set_engine(\"earth\") "
[15] ""
[16] "test_config_5_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_5_dummies_recipe) %>% "
[19] " add_model(test_config_5_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_5_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) "
[10] ""
[11] "test_config_5_no_dummies_spec <- "
[12] " mars() %>% "
[13] " set_mode(\"classification\") %>% "
[14] " set_engine(\"earth\") "
[15] ""
[16] "test_config_5_no_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_5_no_dummies_recipe) %>% "
[19] " add_model(test_config_5_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_6_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Regularization methods sum up functions of the model slope "
[10] " ## coefficients. Because of this, the predictor variables should be on "
[11] " ## the same scale. Before centering and scaling the numeric predictors, "
[12] " ## any predictors with a single unique value are filtered out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_6_dummies_spec <- "
[17] " linear_reg() %>% "
[18] " set_mode(\"regression\") %>% "
[19] " set_engine(\"glmnet\") "
[20] ""
[21] "test_config_6_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_6_dummies_recipe) %>% "
[24] " add_model(test_config_6_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_6_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Regularization methods sum up functions of the model slope "
[10] " ## coefficients. Because of this, the predictor variables should be on "
[11] " ## the same scale. Before centering and scaling the numeric predictors, "
[12] " ## any predictors with a single unique value are filtered out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_6_no_dummies_spec <- "
[17] " multinom_reg() %>% "
[18] " set_mode(\"classification\") %>% "
[19] " set_engine(\"glmnet\") "
[20] ""
[21] "test_config_6_no_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_6_no_dummies_recipe) %>% "
[24] " add_model(test_config_6_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_7_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_7_dummies_spec <- "
[11] " svm_poly() %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_7_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_7_dummies_recipe) %>% "
[17] " add_model(test_config_7_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_7_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_7_no_dummies_spec <- "
[11] " svm_poly() %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_7_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_7_no_dummies_recipe) %>% "
[17] " add_model(test_config_7_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_8_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_8_dummies_spec <- "
[11] " svm_rbf() %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_8_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_8_dummies_recipe) %>% "
[17] " add_model(test_config_8_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_8_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_8_no_dummies_spec <- "
[11] " svm_rbf() %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_8_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_8_no_dummies_recipe) %>% "
[17] " add_model(test_config_8_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_9_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Since distance calculations are used, the predictor variables should "
[10] " ## be on the same scale. Before centering and scaling the numeric "
[11] " ## predictors, any predictors with a single unique value are filtered "
[12] " ## out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_9_dummies_spec <- "
[17] " nearest_neighbor() %>% "
[18] " set_mode(\"regression\") %>% "
[19] " set_engine(\"kknn\") "
[20] ""
[21] "test_config_9_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_9_dummies_recipe) %>% "
[24] " add_model(test_config_9_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_9_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Since distance calculations are used, the predictor variables should "
[10] " ## be on the same scale. Before centering and scaling the numeric "
[11] " ## predictors, any predictors with a single unique value are filtered "
[12] " ## out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_9_no_dummies_spec <- "
[17] " nearest_neighbor() %>% "
[18] " set_mode(\"classification\") %>% "
[19] " set_engine(\"kknn\") "
[20] ""
[21] "test_config_9_no_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_9_no_dummies_recipe) %>% "
[24] " add_model(test_config_9_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_10_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_10_dummies_spec <- "
[5] " gen_additive_mod() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_10_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_10_dummies_recipe) %>% "
[12] " add_model(test_config_10_dummies_spec, formula = stop(\"add your gam formula\")) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_10_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_10_no_dummies_spec <- "
[5] " gen_additive_mod() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_10_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_10_no_dummies_recipe) %>% "
[12] " add_model(test_config_10_no_dummies_spec, formula = stop(\"add your gam formula\")) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_11_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_11_dummies_spec <- "
[15] " pls() %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"mixOmics\") "
[18] ""
[19] "test_config_11_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_11_dummies_recipe) %>% "
[22] " add_model(test_config_11_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_11_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_11_no_dummies_spec <- "
[15] " pls() %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"mixOmics\") "
[18] ""
[19] "test_config_11_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_11_no_dummies_recipe) %>% "
[22] " add_model(test_config_11_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_12_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) %>% "
[10] " step_normalize(all_numeric_predictors()) "
[11] ""
[12] "test_config_12_dummies_spec <- "
[13] " mlp() %>% "
[14] " set_mode(\"regression\") "
[15] ""
[16] "test_config_12_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_12_dummies_recipe) %>% "
[19] " add_model(test_config_12_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_12_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) %>% "
[10] " step_normalize(all_numeric_predictors()) "
[11] ""
[12] "test_config_12_no_dummies_spec <- "
[13] " mlp() %>% "
[14] " set_mode(\"classification\") "
[15] ""
[16] "test_config_12_no_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_12_no_dummies_recipe) %>% "
[19] " add_model(test_config_12_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_13_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_13_dummies_spec <- "
[5] " rand_forest(trees = 1000) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_13_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_13_dummies_recipe) %>% "
[12] " add_model(test_config_13_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_13_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_13_no_dummies_spec <- "
[5] " rand_forest(trees = 1000) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_13_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_13_no_dummies_recipe) %>% "
[12] " add_model(test_config_13_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_14_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_14_dummies_spec <- "
[5] " decision_tree() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_14_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_14_dummies_recipe) %>% "
[12] " add_model(test_config_14_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_14_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_14_no_dummies_spec <- "
[5] " decision_tree() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_14_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_14_no_dummies_recipe) %>% "
[12] " add_model(test_config_14_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_15_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. However, for this model, binary indicator variables can be "
[8] " ## made for each of the levels of the factors (known as 'one-hot "
[9] " ## encoding'). "
[10] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[11] " step_zv(all_predictors()) "
[12] ""
[13] "test_config_15_dummies_spec <- "
[14] " boost_tree() %>% "
[15] " set_mode(\"regression\") %>% "
[16] " set_engine(\"xgboost\") "
[17] ""
[18] "test_config_15_dummies_workflow <- "
[19] " workflow() %>% "
[20] " add_recipe(test_config_15_dummies_recipe) %>% "
[21] " add_model(test_config_15_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_15_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. However, for this model, binary indicator variables can be "
[8] " ## made for each of the levels of the factors (known as 'one-hot "
[9] " ## encoding'). "
[10] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[11] " step_zv(all_predictors()) "
[12] ""
[13] "test_config_15_no_dummies_spec <- "
[14] " boost_tree() %>% "
[15] " set_mode(\"classification\") %>% "
[16] " set_engine(\"xgboost\") "
[17] ""
[18] "test_config_15_no_dummies_workflow <- "
[19] " workflow() %>% "
[20] " add_recipe(test_config_15_no_dummies_recipe) %>% "
[21] " add_model(test_config_15_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_16_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_16_dummies_spec <- "
[15] " rule_fit() %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"xrf\") "
[18] ""
[19] "test_config_16_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_16_dummies_recipe) %>% "
[22] " add_model(test_config_16_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_16_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_16_no_dummies_spec <- "
[15] " rule_fit() %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"xrf\") "
[18] ""
[19] "test_config_16_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_16_no_dummies_recipe) %>% "
[22] " add_model(test_config_16_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_17_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) "
[5] ""
[6] "test_config_17_dummies_spec <- "
[7] " bag_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[8] " set_mode(\"regression\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_17_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_17_dummies_recipe) %>% "
[14] " add_model(test_config_17_dummies_spec) "
[15] ""
[16] "set.seed(27246)"
[17] "test_config_17_dummies_tune <-"
[18] " tune_grid(test_config_17_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[19] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_17_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) "
[5] ""
[6] "test_config_17_no_dummies_spec <- "
[7] " bag_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[8] " set_mode(\"classification\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_17_no_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_17_no_dummies_recipe) %>% "
[14] " add_model(test_config_17_no_dummies_spec) "
[15] ""
[16] "set.seed(27246)"
[17] "test_config_17_no_dummies_tune <-"
[18] " tune_grid(test_config_17_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[19] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_18_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_18_no_dummies_spec <- "
[5] " boost_tree(trees = tune(), min_n = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"C5.0\") "
[8] ""
[9] "test_config_18_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_18_no_dummies_recipe) %>% "
[12] " add_model(test_config_18_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_18_no_dummies_tune <-"
[16] " tune_grid(test_config_18_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_19_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_19_dummies_spec <- "
[8] " cubist_rules(committees = tune(), neighbors = tune()) %>% "
[9] " set_engine(\"Cubist\") "
[10] ""
[11] "test_config_19_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_19_dummies_recipe) %>% "
[14] " add_model(test_config_19_dummies_spec) "
[15] ""
[16] "test_config_19_dummies_grid <- tidyr::crossing(committees = c(1:9, (1:5) * "
[17] " 10), neighbors = c(0, 3, 6, 9)) "
[18] ""
[19] "test_config_19_dummies_tune <- "
[20] " tune_grid(test_config_19_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = test_config_19_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_20_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_20_dummies_spec <- "
[5] " bart(trees = tune(), prior_terminal_node_coef = tune(), prior_terminal_node_expo = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_20_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_20_dummies_recipe) %>% "
[12] " add_model(test_config_20_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_20_dummies_tune <-"
[16] " tune_grid(test_config_20_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_20_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_20_no_dummies_spec <- "
[5] " bart(trees = tune(), prior_terminal_node_coef = tune(), prior_terminal_node_expo = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_20_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_20_no_dummies_recipe) %>% "
[12] " add_model(test_config_20_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_20_no_dummies_tune <-"
[16] " tune_grid(test_config_20_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Output
## MARS models can make predictions on many _sub_models_, meaning that we
## can evaluate many values of `num_terms` without much computational
## cost. A regular grid is used to exploit this property. The first term
## is only the intercept, so the grid is a sequence of even numbered
## values.
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_21_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) "
[10] ""
[11] "test_config_21_dummies_spec <- "
[12] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[13] " set_mode(\"regression\") %>% "
[14] " set_engine(\"earth\") "
[15] ""
[16] "test_config_21_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_21_dummies_recipe) %>% "
[19] " add_model(test_config_21_dummies_spec) "
[20] ""
[21] "test_config_21_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[22] ""
[23] "test_config_21_dummies_tune <- "
[24] " tune_grid(test_config_21_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[25] " grid = test_config_21_dummies_grid) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Output
## MARS models can make predictions on many _sub_models_, meaning that we
## can evaluate many values of `num_terms` without much computational
## cost. A regular grid is used to exploit this property. The first term
## is only the intercept, so the grid is a sequence of even numbered
## values.
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_21_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) "
[10] ""
[11] "test_config_21_no_dummies_spec <- "
[12] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[13] " set_mode(\"classification\") %>% "
[14] " set_engine(\"earth\") "
[15] ""
[16] "test_config_21_no_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_21_no_dummies_recipe) %>% "
[19] " add_model(test_config_21_no_dummies_spec) "
[20] ""
[21] "test_config_21_no_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[22] ""
[23] "test_config_21_no_dummies_tune <- "
[24] " tune_grid(test_config_21_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[25] " grid = test_config_21_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_22_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Regularization methods sum up functions of the model slope "
[10] " ## coefficients. Because of this, the predictor variables should be on "
[11] " ## the same scale. Before centering and scaling the numeric predictors, "
[12] " ## any predictors with a single unique value are filtered out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_22_dummies_spec <- "
[17] " linear_reg(penalty = tune(), mixture = tune()) %>% "
[18] " set_mode(\"regression\") %>% "
[19] " set_engine(\"glmnet\") "
[20] ""
[21] "test_config_22_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_22_dummies_recipe) %>% "
[24] " add_model(test_config_22_dummies_spec) "
[25] ""
[26] "test_config_22_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20), "
[27] " mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[28] ""
[29] "test_config_22_dummies_tune <- "
[30] " tune_grid(test_config_22_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[31] " grid = test_config_22_dummies_grid) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_22_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Regularization methods sum up functions of the model slope "
[10] " ## coefficients. Because of this, the predictor variables should be on "
[11] " ## the same scale. Before centering and scaling the numeric predictors, "
[12] " ## any predictors with a single unique value are filtered out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_22_no_dummies_spec <- "
[17] " multinom_reg(penalty = tune(), mixture = tune()) %>% "
[18] " set_mode(\"classification\") %>% "
[19] " set_engine(\"glmnet\") "
[20] ""
[21] "test_config_22_no_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_22_no_dummies_recipe) %>% "
[24] " add_model(test_config_22_no_dummies_spec) "
[25] ""
[26] "test_config_22_no_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, "
[27] " length.out = 20), mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[28] ""
[29] "test_config_22_no_dummies_tune <- "
[30] " tune_grid(test_config_22_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[31] " grid = test_config_22_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_23_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_23_dummies_spec <- "
[11] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_23_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_23_dummies_recipe) %>% "
[17] " add_model(test_config_23_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_23_dummies_tune <-"
[21] " tune_grid(test_config_23_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_23_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_23_no_dummies_spec <- "
[11] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_23_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_23_no_dummies_recipe) %>% "
[17] " add_model(test_config_23_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_23_no_dummies_tune <-"
[21] " tune_grid(test_config_23_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_24_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_24_dummies_spec <- "
[11] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_24_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_24_dummies_recipe) %>% "
[17] " add_model(test_config_24_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_24_dummies_tune <-"
[21] " tune_grid(test_config_24_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_24_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " ## Since dot product calculations are used, the predictor variables "
[4] " ## should be on the same scale. Before centering and scaling the numeric "
[5] " ## predictors, any predictors with a single unique value are filtered "
[6] " ## out. "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_24_no_dummies_spec <- "
[11] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_24_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_24_no_dummies_recipe) %>% "
[17] " add_model(test_config_24_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_24_no_dummies_tune <-"
[21] " tune_grid(test_config_24_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_25_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Since distance calculations are used, the predictor variables should "
[10] " ## be on the same scale. Before centering and scaling the numeric "
[11] " ## predictors, any predictors with a single unique value are filtered "
[12] " ## out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_25_dummies_spec <- "
[17] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[18] " set_mode(\"regression\") %>% "
[19] " set_engine(\"kknn\") "
[20] ""
[21] "test_config_25_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_25_dummies_recipe) %>% "
[24] " add_model(test_config_25_dummies_spec) "
[25] ""
[26] "set.seed(27246)"
[27] "test_config_25_dummies_tune <-"
[28] " tune_grid(test_config_25_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[29] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_25_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " ## Since distance calculations are used, the predictor variables should "
[10] " ## be on the same scale. Before centering and scaling the numeric "
[11] " ## predictors, any predictors with a single unique value are filtered "
[12] " ## out. "
[13] " step_zv(all_predictors()) %>% "
[14] " step_normalize(all_numeric_predictors()) "
[15] ""
[16] "test_config_25_no_dummies_spec <- "
[17] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[18] " set_mode(\"classification\") %>% "
[19] " set_engine(\"kknn\") "
[20] ""
[21] "test_config_25_no_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_25_no_dummies_recipe) %>% "
[24] " add_model(test_config_25_no_dummies_spec) "
[25] ""
[26] "set.seed(27246)"
[27] "test_config_25_no_dummies_tune <-"
[28] " tune_grid(test_config_25_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[29] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_26_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_26_dummies_spec <- "
[5] " gen_additive_mod(select_features = tune(), adjust_deg_free = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_26_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_26_dummies_recipe) %>% "
[12] " add_model(test_config_26_dummies_spec, formula = stop(\"add your gam formula\")) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_26_dummies_tune <-"
[16] " tune_grid(test_config_26_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_26_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_26_no_dummies_spec <- "
[5] " gen_additive_mod(select_features = tune(), adjust_deg_free = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_26_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_26_no_dummies_recipe) %>% "
[12] " add_model(test_config_26_no_dummies_spec, formula = stop(\"add your gam formula\")) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_26_no_dummies_tune <-"
[16] " tune_grid(test_config_26_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_27_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_27_dummies_spec <- "
[15] " pls(predictor_prop = tune(), num_comp = tune()) %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"mixOmics\") "
[18] ""
[19] "test_config_27_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_27_dummies_recipe) %>% "
[22] " add_model(test_config_27_dummies_spec) "
[23] ""
[24] "set.seed(27246)"
[25] "test_config_27_dummies_tune <-"
[26] " tune_grid(test_config_27_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[27] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_27_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_27_no_dummies_spec <- "
[15] " pls(predictor_prop = tune(), num_comp = tune()) %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"mixOmics\") "
[18] ""
[19] "test_config_27_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_27_no_dummies_recipe) %>% "
[22] " add_model(test_config_27_no_dummies_spec) "
[23] ""
[24] "set.seed(27246)"
[25] "test_config_27_no_dummies_tune <-"
[26] " tune_grid(test_config_27_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[27] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_28_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) %>% "
[10] " step_normalize(all_numeric_predictors()) "
[11] ""
[12] "test_config_28_dummies_spec <- "
[13] " mlp(hidden_units = tune(), penalty = tune(), epochs = tune()) %>% "
[14] " set_mode(\"regression\") "
[15] ""
[16] "test_config_28_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_28_dummies_recipe) %>% "
[19] " add_model(test_config_28_dummies_spec) "
[20] ""
[21] "set.seed(27246)"
[22] "test_config_28_dummies_tune <-"
[23] " tune_grid(test_config_28_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_28_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. "
[8] " step_dummy(all_nominal_predictors()) %>% "
[9] " step_zv(all_predictors()) %>% "
[10] " step_normalize(all_numeric_predictors()) "
[11] ""
[12] "test_config_28_no_dummies_spec <- "
[13] " mlp(hidden_units = tune(), penalty = tune(), epochs = tune()) %>% "
[14] " set_mode(\"classification\") "
[15] ""
[16] "test_config_28_no_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_28_no_dummies_recipe) %>% "
[19] " add_model(test_config_28_no_dummies_spec) "
[20] ""
[21] "set.seed(27246)"
[22] "test_config_28_no_dummies_tune <-"
[23] " tune_grid(test_config_28_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_29_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_29_dummies_spec <- "
[5] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_29_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_29_dummies_recipe) %>% "
[12] " add_model(test_config_29_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_29_dummies_tune <-"
[16] " tune_grid(test_config_29_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_29_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_29_no_dummies_spec <- "
[5] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_29_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_29_no_dummies_recipe) %>% "
[12] " add_model(test_config_29_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_29_no_dummies_tune <-"
[16] " tune_grid(test_config_29_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_30_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_30_dummies_spec <- "
[5] " decision_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_30_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_30_dummies_recipe) %>% "
[12] " add_model(test_config_30_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_30_dummies_tune <-"
[16] " tune_grid(test_config_30_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_30_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_30_no_dummies_spec <- "
[5] " decision_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_30_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_30_no_dummies_recipe) %>% "
[12] " add_model(test_config_30_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_30_no_dummies_tune <-"
[16] " tune_grid(test_config_30_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_31_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. However, for this model, binary indicator variables can be "
[8] " ## made for each of the levels of the factors (known as 'one-hot "
[9] " ## encoding'). "
[10] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[11] " step_zv(all_predictors()) "
[12] ""
[13] "test_config_31_dummies_spec <- "
[14] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[15] " loss_reduction = tune(), sample_size = tune()) %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"xgboost\") "
[18] ""
[19] "test_config_31_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_31_dummies_recipe) %>% "
[22] " add_model(test_config_31_dummies_spec) "
[23] ""
[24] "set.seed(27246)"
[25] "test_config_31_dummies_tune <-"
[26] " tune_grid(test_config_31_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[27] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_31_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " ## This model requires the predictors to be numeric. The most common "
[5] " ## method to convert qualitative predictors to numeric is to create "
[6] " ## binary indicator variables (aka dummy variables) from these "
[7] " ## predictors. However, for this model, binary indicator variables can be "
[8] " ## made for each of the levels of the factors (known as 'one-hot "
[9] " ## encoding'). "
[10] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[11] " step_zv(all_predictors()) "
[12] ""
[13] "test_config_31_no_dummies_spec <- "
[14] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[15] " loss_reduction = tune(), sample_size = tune()) %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"xgboost\") "
[18] ""
[19] "test_config_31_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_31_no_dummies_recipe) %>% "
[22] " add_model(test_config_31_no_dummies_spec) "
[23] ""
[24] "set.seed(27246)"
[25] "test_config_31_no_dummies_tune <-"
[26] " tune_grid(test_config_31_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[27] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_32_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_32_dummies_spec <- "
[15] " rule_fit(mtry = tune(), trees = tune(), min_n = tune(), tree_depth = tune(), "
[16] " learn_rate = tune(), loss_reduction = tune(), sample_size = tune(), penalty = tune()) %>% "
[17] " set_mode(\"regression\") %>% "
[18] " set_engine(\"xrf\") "
[19] ""
[20] "test_config_32_dummies_workflow <- "
[21] " workflow() %>% "
[22] " add_recipe(test_config_32_dummies_recipe) %>% "
[23] " add_model(test_config_32_dummies_spec) "
[24] ""
[25] "set.seed(27246)"
[26] "test_config_32_dummies_tune <-"
[27] " tune_grid(test_config_32_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[28] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_32_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " ## This model requires the predictors to be numeric. The most common "
[7] " ## method to convert qualitative predictors to numeric is to create "
[8] " ## binary indicator variables (aka dummy variables) from these "
[9] " ## predictors. "
[10] " step_dummy(all_nominal_predictors()) %>% "
[11] " step_zv(all_predictors()) %>% "
[12] " step_normalize(all_numeric_predictors()) "
[13] ""
[14] "test_config_32_no_dummies_spec <- "
[15] " rule_fit(mtry = tune(), trees = tune(), min_n = tune(), tree_depth = tune(), "
[16] " learn_rate = tune(), loss_reduction = tune(), sample_size = tune(), penalty = tune()) %>% "
[17] " set_mode(\"classification\") %>% "
[18] " set_engine(\"xrf\") "
[19] ""
[20] "test_config_32_no_dummies_workflow <- "
[21] " workflow() %>% "
[22] " add_recipe(test_config_32_no_dummies_recipe) %>% "
[23] " add_model(test_config_32_no_dummies_spec) "
[24] ""
[25] "set.seed(27246)"
[26] "test_config_32_no_dummies_tune <-"
[27] " tune_grid(test_config_32_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[28] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_33_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) "
[5] ""
[6] "test_config_33_dummies_spec <- "
[7] " bag_tree() %>% "
[8] " set_mode(\"regression\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_33_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_33_dummies_recipe) %>% "
[14] " add_model(test_config_33_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_33_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) "
[5] ""
[6] "test_config_33_no_dummies_spec <- "
[7] " bag_tree() %>% "
[8] " set_mode(\"classification\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_33_no_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_33_no_dummies_recipe) %>% "
[14] " add_model(test_config_33_no_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_34_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_34_no_dummies_spec <- "
[5] " boost_tree() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"C5.0\") "
[8] ""
[9] "test_config_34_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_34_no_dummies_recipe) %>% "
[12] " add_model(test_config_34_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_35_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_35_dummies_spec <- "
[8] " cubist_rules() %>% "
[9] " set_engine(\"Cubist\") "
[10] ""
[11] "test_config_35_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_35_dummies_recipe) %>% "
[14] " add_model(test_config_35_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_36_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_36_dummies_spec <- "
[5] " bart() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_36_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_36_dummies_recipe) %>% "
[12] " add_model(test_config_36_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_36_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_36_no_dummies_spec <- "
[5] " bart() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_36_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_36_no_dummies_recipe) %>% "
[12] " add_model(test_config_36_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_37_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_37_dummies_spec <- "
[8] " mars() %>% "
[9] " set_mode(\"regression\") %>% "
[10] " set_engine(\"earth\") "
[11] ""
[12] "test_config_37_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_37_dummies_recipe) %>% "
[15] " add_model(test_config_37_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_37_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_37_no_dummies_spec <- "
[8] " mars() %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"earth\") "
[11] ""
[12] "test_config_37_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_37_no_dummies_recipe) %>% "
[15] " add_model(test_config_37_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_38_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_38_dummies_spec <- "
[9] " linear_reg() %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"glmnet\") "
[12] ""
[13] "test_config_38_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_38_dummies_recipe) %>% "
[16] " add_model(test_config_38_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_38_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_38_no_dummies_spec <- "
[9] " multinom_reg() %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"glmnet\") "
[12] ""
[13] "test_config_38_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_38_no_dummies_recipe) %>% "
[16] " add_model(test_config_38_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_39_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_39_dummies_spec <- "
[7] " svm_poly() %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_39_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_39_dummies_recipe) %>% "
[13] " add_model(test_config_39_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_39_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_39_no_dummies_spec <- "
[7] " svm_poly() %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_39_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_39_no_dummies_recipe) %>% "
[13] " add_model(test_config_39_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_40_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_40_dummies_spec <- "
[7] " svm_rbf() %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_40_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_40_dummies_recipe) %>% "
[13] " add_model(test_config_40_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_40_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_40_no_dummies_spec <- "
[7] " svm_rbf() %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_40_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_40_no_dummies_recipe) %>% "
[13] " add_model(test_config_40_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_41_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_41_dummies_spec <- "
[9] " nearest_neighbor() %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"kknn\") "
[12] ""
[13] "test_config_41_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_41_dummies_recipe) %>% "
[16] " add_model(test_config_41_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_41_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_41_no_dummies_spec <- "
[9] " nearest_neighbor() %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"kknn\") "
[12] ""
[13] "test_config_41_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_41_no_dummies_recipe) %>% "
[16] " add_model(test_config_41_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_42_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_42_dummies_spec <- "
[5] " gen_additive_mod() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_42_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_42_dummies_recipe) %>% "
[12] " add_model(test_config_42_dummies_spec, formula = stop(\"add your gam formula\")) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_42_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_42_no_dummies_spec <- "
[5] " gen_additive_mod() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_42_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_42_no_dummies_recipe) %>% "
[12] " add_model(test_config_42_no_dummies_spec, formula = stop(\"add your gam formula\")) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_43_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_43_dummies_spec <- "
[11] " pls() %>% "
[12] " set_mode(\"regression\") %>% "
[13] " set_engine(\"mixOmics\") "
[14] ""
[15] "test_config_43_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_43_dummies_recipe) %>% "
[18] " add_model(test_config_43_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_43_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_43_no_dummies_spec <- "
[11] " pls() %>% "
[12] " set_mode(\"classification\") %>% "
[13] " set_engine(\"mixOmics\") "
[14] ""
[15] "test_config_43_no_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_43_no_dummies_recipe) %>% "
[18] " add_model(test_config_43_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_44_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_44_dummies_spec <- "
[9] " mlp() %>% "
[10] " set_mode(\"regression\") "
[11] ""
[12] "test_config_44_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_44_dummies_recipe) %>% "
[15] " add_model(test_config_44_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_44_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_44_no_dummies_spec <- "
[9] " mlp() %>% "
[10] " set_mode(\"classification\") "
[11] ""
[12] "test_config_44_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_44_no_dummies_recipe) %>% "
[15] " add_model(test_config_44_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_45_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_45_dummies_spec <- "
[5] " rand_forest(trees = 1000) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_45_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_45_dummies_recipe) %>% "
[12] " add_model(test_config_45_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_45_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_45_no_dummies_spec <- "
[5] " rand_forest(trees = 1000) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_45_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_45_no_dummies_recipe) %>% "
[12] " add_model(test_config_45_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_46_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_46_dummies_spec <- "
[5] " decision_tree() %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_46_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_46_dummies_recipe) %>% "
[12] " add_model(test_config_46_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_46_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_46_no_dummies_spec <- "
[5] " decision_tree() %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_46_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_46_no_dummies_recipe) %>% "
[12] " add_model(test_config_46_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_47_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_47_dummies_spec <- "
[8] " boost_tree() %>% "
[9] " set_mode(\"regression\") %>% "
[10] " set_engine(\"xgboost\") "
[11] ""
[12] "test_config_47_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_47_dummies_recipe) %>% "
[15] " add_model(test_config_47_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_47_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_47_no_dummies_spec <- "
[8] " boost_tree() %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"xgboost\") "
[11] ""
[12] "test_config_47_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_47_no_dummies_recipe) %>% "
[15] " add_model(test_config_47_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_48_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_48_dummies_spec <- "
[11] " rule_fit() %>% "
[12] " set_mode(\"regression\") %>% "
[13] " set_engine(\"xrf\") "
[14] ""
[15] "test_config_48_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_48_dummies_recipe) %>% "
[18] " add_model(test_config_48_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_48_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_48_no_dummies_spec <- "
[11] " rule_fit() %>% "
[12] " set_mode(\"classification\") %>% "
[13] " set_engine(\"xrf\") "
[14] ""
[15] "test_config_48_no_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_48_no_dummies_recipe) %>% "
[18] " add_model(test_config_48_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_49_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) "
[5] ""
[6] "test_config_49_dummies_spec <- "
[7] " bag_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[8] " set_mode(\"regression\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_49_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_49_dummies_recipe) %>% "
[14] " add_model(test_config_49_dummies_spec) "
[15] ""
[16] "set.seed(27246)"
[17] "test_config_49_dummies_tune <-"
[18] " tune_grid(test_config_49_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[19] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(baguette)"
[2] ""
[3] "test_config_49_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) "
[5] ""
[6] "test_config_49_no_dummies_spec <- "
[7] " bag_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[8] " set_mode(\"classification\") %>% "
[9] " set_engine(\"rpart\") "
[10] ""
[11] "test_config_49_no_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_49_no_dummies_recipe) %>% "
[14] " add_model(test_config_49_no_dummies_spec) "
[15] ""
[16] "set.seed(27246)"
[17] "test_config_49_no_dummies_tune <-"
[18] " tune_grid(test_config_49_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[19] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_50_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_50_no_dummies_spec <- "
[5] " boost_tree(trees = tune(), min_n = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"C5.0\") "
[8] ""
[9] "test_config_50_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_50_no_dummies_recipe) %>% "
[12] " add_model(test_config_50_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_50_no_dummies_tune <-"
[16] " tune_grid(test_config_50_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_51_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_51_dummies_spec <- "
[8] " cubist_rules(committees = tune(), neighbors = tune()) %>% "
[9] " set_engine(\"Cubist\") "
[10] ""
[11] "test_config_51_dummies_workflow <- "
[12] " workflow() %>% "
[13] " add_recipe(test_config_51_dummies_recipe) %>% "
[14] " add_model(test_config_51_dummies_spec) "
[15] ""
[16] "test_config_51_dummies_grid <- tidyr::crossing(committees = c(1:9, (1:5) * "
[17] " 10), neighbors = c(0, 3, 6, 9)) "
[18] ""
[19] "test_config_51_dummies_tune <- "
[20] " tune_grid(test_config_51_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = test_config_51_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_52_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_52_dummies_spec <- "
[5] " bart(trees = tune(), prior_terminal_node_coef = tune(), prior_terminal_node_expo = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_52_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_52_dummies_recipe) %>% "
[12] " add_model(test_config_52_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_52_dummies_tune <-"
[16] " tune_grid(test_config_52_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_52_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_52_no_dummies_spec <- "
[5] " bart(trees = tune(), prior_terminal_node_coef = tune(), prior_terminal_node_expo = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"dbarts\") "
[8] ""
[9] "test_config_52_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_52_no_dummies_recipe) %>% "
[12] " add_model(test_config_52_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_52_no_dummies_tune <-"
[16] " tune_grid(test_config_52_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_53_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_53_dummies_spec <- "
[8] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[9] " set_mode(\"regression\") %>% "
[10] " set_engine(\"earth\") "
[11] ""
[12] "test_config_53_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_53_dummies_recipe) %>% "
[15] " add_model(test_config_53_dummies_spec) "
[16] ""
[17] "test_config_53_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[18] ""
[19] "test_config_53_dummies_tune <- "
[20] " tune_grid(test_config_53_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = test_config_53_dummies_grid) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_53_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_53_no_dummies_spec <- "
[8] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"earth\") "
[11] ""
[12] "test_config_53_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_53_no_dummies_recipe) %>% "
[15] " add_model(test_config_53_no_dummies_spec) "
[16] ""
[17] "test_config_53_no_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[18] ""
[19] "test_config_53_no_dummies_tune <- "
[20] " tune_grid(test_config_53_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = test_config_53_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_54_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_54_dummies_spec <- "
[9] " linear_reg(penalty = tune(), mixture = tune()) %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"glmnet\") "
[12] ""
[13] "test_config_54_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_54_dummies_recipe) %>% "
[16] " add_model(test_config_54_dummies_spec) "
[17] ""
[18] "test_config_54_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20), "
[19] " mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[20] ""
[21] "test_config_54_dummies_tune <- "
[22] " tune_grid(test_config_54_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[23] " grid = test_config_54_dummies_grid) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_54_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_54_no_dummies_spec <- "
[9] " multinom_reg(penalty = tune(), mixture = tune()) %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"glmnet\") "
[12] ""
[13] "test_config_54_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_54_no_dummies_recipe) %>% "
[16] " add_model(test_config_54_no_dummies_spec) "
[17] ""
[18] "test_config_54_no_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, "
[19] " length.out = 20), mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[20] ""
[21] "test_config_54_no_dummies_tune <- "
[22] " tune_grid(test_config_54_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[23] " grid = test_config_54_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_55_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_55_dummies_spec <- "
[7] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_55_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_55_dummies_recipe) %>% "
[13] " add_model(test_config_55_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_55_dummies_tune <-"
[17] " tune_grid(test_config_55_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[18] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_55_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_55_no_dummies_spec <- "
[7] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_55_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_55_no_dummies_recipe) %>% "
[13] " add_model(test_config_55_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_55_no_dummies_tune <-"
[17] " tune_grid(test_config_55_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[18] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_56_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_56_dummies_spec <- "
[7] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_56_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_56_dummies_recipe) %>% "
[13] " add_model(test_config_56_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_56_dummies_tune <-"
[17] " tune_grid(test_config_56_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[18] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_56_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_56_no_dummies_spec <- "
[7] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_56_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_56_no_dummies_recipe) %>% "
[13] " add_model(test_config_56_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_56_no_dummies_tune <-"
[17] " tune_grid(test_config_56_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[18] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_57_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_57_dummies_spec <- "
[9] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"kknn\") "
[12] ""
[13] "test_config_57_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_57_dummies_recipe) %>% "
[16] " add_model(test_config_57_dummies_spec) "
[17] ""
[18] "set.seed(27246)"
[19] "test_config_57_dummies_tune <-"
[20] " tune_grid(test_config_57_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_57_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_57_no_dummies_spec <- "
[9] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"kknn\") "
[12] ""
[13] "test_config_57_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_57_no_dummies_recipe) %>% "
[16] " add_model(test_config_57_no_dummies_spec) "
[17] ""
[18] "set.seed(27246)"
[19] "test_config_57_no_dummies_tune <-"
[20] " tune_grid(test_config_57_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_58_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_58_dummies_spec <- "
[5] " gen_additive_mod(select_features = tune(), adjust_deg_free = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_58_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_58_dummies_recipe) %>% "
[12] " add_model(test_config_58_dummies_spec, formula = stop(\"add your gam formula\")) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_58_dummies_tune <-"
[16] " tune_grid(test_config_58_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_58_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_58_no_dummies_spec <- "
[5] " gen_additive_mod(select_features = tune(), adjust_deg_free = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"mgcv\") "
[8] ""
[9] "test_config_58_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_58_no_dummies_recipe) %>% "
[12] " add_model(test_config_58_no_dummies_spec, formula = stop(\"add your gam formula\")) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_58_no_dummies_tune <-"
[16] " tune_grid(test_config_58_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_59_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_59_dummies_spec <- "
[11] " pls(predictor_prop = tune(), num_comp = tune()) %>% "
[12] " set_mode(\"regression\") %>% "
[13] " set_engine(\"mixOmics\") "
[14] ""
[15] "test_config_59_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_59_dummies_recipe) %>% "
[18] " add_model(test_config_59_dummies_spec) "
[19] ""
[20] "set.seed(27246)"
[21] "test_config_59_dummies_tune <-"
[22] " tune_grid(test_config_59_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[23] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(plsmod)"
[2] ""
[3] "test_config_59_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_59_no_dummies_spec <- "
[11] " pls(predictor_prop = tune(), num_comp = tune()) %>% "
[12] " set_mode(\"classification\") %>% "
[13] " set_engine(\"mixOmics\") "
[14] ""
[15] "test_config_59_no_dummies_workflow <- "
[16] " workflow() %>% "
[17] " add_recipe(test_config_59_no_dummies_recipe) %>% "
[18] " add_model(test_config_59_no_dummies_spec) "
[19] ""
[20] "set.seed(27246)"
[21] "test_config_59_no_dummies_tune <-"
[22] " tune_grid(test_config_59_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[23] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_60_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_60_dummies_spec <- "
[9] " mlp(hidden_units = tune(), penalty = tune(), epochs = tune()) %>% "
[10] " set_mode(\"regression\") "
[11] ""
[12] "test_config_60_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_60_dummies_recipe) %>% "
[15] " add_model(test_config_60_dummies_spec) "
[16] ""
[17] "set.seed(27246)"
[18] "test_config_60_dummies_tune <-"
[19] " tune_grid(test_config_60_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[20] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_60_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors()) %>% "
[5] " step_zv(all_predictors()) %>% "
[6] " step_normalize(all_numeric_predictors()) "
[7] ""
[8] "test_config_60_no_dummies_spec <- "
[9] " mlp(hidden_units = tune(), penalty = tune(), epochs = tune()) %>% "
[10] " set_mode(\"classification\") "
[11] ""
[12] "test_config_60_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_60_no_dummies_recipe) %>% "
[15] " add_model(test_config_60_no_dummies_spec) "
[16] ""
[17] "set.seed(27246)"
[18] "test_config_60_no_dummies_tune <-"
[19] " tune_grid(test_config_60_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[20] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_61_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_61_dummies_spec <- "
[5] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_61_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_61_dummies_recipe) %>% "
[12] " add_model(test_config_61_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_61_dummies_tune <-"
[16] " tune_grid(test_config_61_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_61_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_61_no_dummies_spec <- "
[5] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"ranger\") "
[8] ""
[9] "test_config_61_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_61_no_dummies_recipe) %>% "
[12] " add_model(test_config_61_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_61_no_dummies_tune <-"
[16] " tune_grid(test_config_61_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_62_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) "
[3] ""
[4] "test_config_62_dummies_spec <- "
[5] " decision_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[6] " set_mode(\"regression\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_62_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_62_dummies_recipe) %>% "
[12] " add_model(test_config_62_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_62_dummies_tune <-"
[16] " tune_grid(test_config_62_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_62_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) "
[3] ""
[4] "test_config_62_no_dummies_spec <- "
[5] " decision_tree(tree_depth = tune(), min_n = tune(), cost_complexity = tune()) %>% "
[6] " set_mode(\"classification\") %>% "
[7] " set_engine(\"rpart\") "
[8] ""
[9] "test_config_62_no_dummies_workflow <- "
[10] " workflow() %>% "
[11] " add_recipe(test_config_62_no_dummies_recipe) %>% "
[12] " add_model(test_config_62_no_dummies_spec) "
[13] ""
[14] "set.seed(27246)"
[15] "test_config_62_no_dummies_tune <-"
[16] " tune_grid(test_config_62_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[17] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_63_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_63_dummies_spec <- "
[8] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[9] " loss_reduction = tune(), sample_size = tune()) %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"xgboost\") "
[12] ""
[13] "test_config_63_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_63_dummies_recipe) %>% "
[16] " add_model(test_config_63_dummies_spec) "
[17] ""
[18] "set.seed(27246)"
[19] "test_config_63_dummies_tune <-"
[20] " tune_grid(test_config_63_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_63_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins) %>% "
[3] " step_novel(all_nominal_predictors()) %>% "
[4] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[5] " step_zv(all_predictors()) "
[6] ""
[7] "test_config_63_no_dummies_spec <- "
[8] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[9] " loss_reduction = tune(), sample_size = tune()) %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"xgboost\") "
[12] ""
[13] "test_config_63_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_63_no_dummies_recipe) %>% "
[16] " add_model(test_config_63_no_dummies_spec) "
[17] ""
[18] "set.seed(27246)"
[19] "test_config_63_no_dummies_tune <-"
[20] " tune_grid(test_config_63_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[21] " grid = stop(\"add number of candidate points\"))"
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_64_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_64_dummies_spec <- "
[11] " rule_fit(mtry = tune(), trees = tune(), min_n = tune(), tree_depth = tune(), "
[12] " learn_rate = tune(), loss_reduction = tune(), sample_size = tune(), penalty = tune()) %>% "
[13] " set_mode(\"regression\") %>% "
[14] " set_engine(\"xrf\") "
[15] ""
[16] "test_config_64_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_64_dummies_recipe) %>% "
[19] " add_model(test_config_64_dummies_spec) "
[20] ""
[21] "set.seed(27246)"
[22] "test_config_64_dummies_tune <-"
[23] " tune_grid(test_config_64_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = stop(\"add number of candidate points\"))"
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_64_no_dummies_recipe <- "
[4] " recipe(formula = species ~ ., data = penguins) %>% "
[5] " step_novel(all_nominal_predictors()) %>% "
[6] " step_dummy(all_nominal_predictors()) %>% "
[7] " step_zv(all_predictors()) %>% "
[8] " step_normalize(all_numeric_predictors()) "
[9] ""
[10] "test_config_64_no_dummies_spec <- "
[11] " rule_fit(mtry = tune(), trees = tune(), min_n = tune(), tree_depth = tune(), "
[12] " learn_rate = tune(), loss_reduction = tune(), sample_size = tune(), penalty = tune()) %>% "
[13] " set_mode(\"classification\") %>% "
[14] " set_engine(\"xrf\") "
[15] ""
[16] "test_config_64_no_dummies_workflow <- "
[17] " workflow() %>% "
[18] " add_recipe(test_config_64_no_dummies_recipe) %>% "
[19] " add_model(test_config_64_no_dummies_spec) "
[20] ""
[21] "set.seed(27246)"
[22] "test_config_64_no_dummies_tune <-"
[23] " tune_grid(test_config_64_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = stop(\"add number of candidate points\"))"
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