Nothing
tests/testthat/_snaps/clipboard.md
In 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] "test_config_1_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Regularization methods sum up functions of the model slope "
[13] " ## coefficients. Because of this, the predictor variables should be on "
[14] " ## the same scale. Before centering and scaling the numeric predictors, "
[15] " ## any predictors with a single unique value are filtered out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_1_dummies_spec <- "
[20] " linear_reg() %>% "
[21] " set_mode(\"regression\") %>% "
[22] " set_engine(\"glmnet\") "
[23] ""
[24] "test_config_1_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_1_dummies_recipe) %>% "
[27] " 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] "test_config_1_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Regularization methods sum up functions of the model slope "
[13] " ## coefficients. Because of this, the predictor variables should be on "
[14] " ## the same scale. Before centering and scaling the numeric predictors, "
[15] " ## any predictors with a single unique value are filtered out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_1_no_dummies_spec <- "
[20] " multinom_reg() %>% "
[21] " set_mode(\"classification\") %>% "
[22] " set_engine(\"glmnet\") "
[23] ""
[24] "test_config_1_no_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_1_no_dummies_recipe) %>% "
[27] " add_model(test_config_1_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_2_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. However, for this model, binary indicator variables can be "
[11] " ## made for each of the levels of the factors (known as 'one-hot "
[12] " ## encoding'). "
[13] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[14] " step_zv(all_predictors()) "
[15] ""
[16] "test_config_2_dummies_spec <- "
[17] " boost_tree() %>% "
[18] " set_mode(\"regression\") %>% "
[19] " set_engine(\"xgboost\") "
[20] ""
[21] "test_config_2_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_2_dummies_recipe) %>% "
[24] " add_model(test_config_2_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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. However, for this model, binary indicator variables can be "
[11] " ## made for each of the levels of the factors (known as 'one-hot "
[12] " ## encoding'). "
[13] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[14] " step_zv(all_predictors()) "
[15] ""
[16] "test_config_2_no_dummies_spec <- "
[17] " boost_tree() %>% "
[18] " set_mode(\"classification\") %>% "
[19] " set_engine(\"xgboost\") "
[20] ""
[21] "test_config_2_no_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_2_no_dummies_recipe) %>% "
[24] " 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] "test_config_3_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_3_dummies_spec <- "
[8] " rand_forest(trees = 1000) %>% "
[9] " set_mode(\"regression\") %>% "
[10] " set_engine(\"ranger\") "
[11] ""
[12] "test_config_3_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_3_dummies_recipe) %>% "
[15] " add_model(test_config_3_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_3_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_3_no_dummies_spec <- "
[8] " rand_forest(trees = 1000) %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"ranger\") "
[11] ""
[12] "test_config_3_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_3_no_dummies_recipe) %>% "
[15] " add_model(test_config_3_no_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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Since distance calculations are used, the predictor variables should "
[13] " ## be on the same scale. Before centering and scaling the numeric "
[14] " ## predictors, any predictors with a single unique value are filtered "
[15] " ## out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_4_dummies_spec <- "
[20] " nearest_neighbor() %>% "
[21] " set_mode(\"regression\") %>% "
[22] " set_engine(\"kknn\") "
[23] ""
[24] "test_config_4_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_4_dummies_recipe) %>% "
[27] " 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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Since distance calculations are used, the predictor variables should "
[13] " ## be on the same scale. Before centering and scaling the numeric "
[14] " ## predictors, any predictors with a single unique value are filtered "
[15] " ## out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_4_no_dummies_spec <- "
[20] " nearest_neighbor() %>% "
[21] " set_mode(\"classification\") %>% "
[22] " set_engine(\"kknn\") "
[23] ""
[24] "test_config_4_no_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_4_no_dummies_recipe) %>% "
[27] " 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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " step_zv(all_predictors()) "
[13] ""
[14] "test_config_5_dummies_spec <- "
[15] " mars() %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"earth\") "
[18] ""
[19] "test_config_5_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_5_dummies_recipe) %>% "
[22] " 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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " step_zv(all_predictors()) "
[13] ""
[14] "test_config_5_no_dummies_spec <- "
[15] " mars() %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"earth\") "
[18] ""
[19] "test_config_5_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_5_no_dummies_recipe) %>% "
[22] " 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] "library(rules)"
[2] ""
[3] "test_config_6_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[5] " ## For modeling, it is preferred to encode qualitative data as factors "
[6] " ## (instead of character). "
[7] " step_string2factor(one_of(\"island\")) %>% "
[8] " step_zv(all_predictors()) "
[9] ""
[10] "test_config_6_dummies_spec <- "
[11] " cubist_rules() %>% "
[12] " set_engine(\"Cubist\") "
[13] ""
[14] "test_config_6_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_6_dummies_recipe) %>% "
[17] " add_model(test_config_6_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, clipboard = TRUE) %>% "
[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, clipboard = TRUE) %>% "
[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, clipboard = TRUE) %>% "
[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, clipboard = TRUE) %>% "
[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
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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_9_no_dummies_spec <- "
[8] " boost_tree() %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"C5.0\") "
[11] ""
[12] "test_config_9_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_9_no_dummies_recipe) %>% "
[15] " 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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Regularization methods sum up functions of the model slope "
[13] " ## coefficients. Because of this, the predictor variables should be on "
[14] " ## the same scale. Before centering and scaling the numeric predictors, "
[15] " ## any predictors with a single unique value are filtered out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_10_dummies_spec <- "
[20] " linear_reg(penalty = tune(), mixture = tune()) %>% "
[21] " set_mode(\"regression\") %>% "
[22] " set_engine(\"glmnet\") "
[23] ""
[24] "test_config_10_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_10_dummies_recipe) %>% "
[27] " add_model(test_config_10_dummies_spec) "
[28] ""
[29] "test_config_10_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20), "
[30] " mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[31] ""
[32] "test_config_10_dummies_tune <- "
[33] " tune_grid(test_config_10_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[34] " grid = test_config_10_dummies_grid) "
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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Regularization methods sum up functions of the model slope "
[13] " ## coefficients. Because of this, the predictor variables should be on "
[14] " ## the same scale. Before centering and scaling the numeric predictors, "
[15] " ## any predictors with a single unique value are filtered out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_10_no_dummies_spec <- "
[20] " multinom_reg(penalty = tune(), mixture = tune()) %>% "
[21] " set_mode(\"classification\") %>% "
[22] " set_engine(\"glmnet\") "
[23] ""
[24] "test_config_10_no_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_10_no_dummies_recipe) %>% "
[27] " add_model(test_config_10_no_dummies_spec) "
[28] ""
[29] "test_config_10_no_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, "
[30] " length.out = 20), mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[31] ""
[32] "test_config_10_no_dummies_tune <- "
[33] " tune_grid(test_config_10_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[34] " grid = test_config_10_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_11_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. However, for this model, binary indicator variables can be "
[11] " ## made for each of the levels of the factors (known as 'one-hot "
[12] " ## encoding'). "
[13] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[14] " step_zv(all_predictors()) "
[15] ""
[16] "test_config_11_dummies_spec <- "
[17] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[18] " loss_reduction = tune(), sample_size = tune()) %>% "
[19] " set_mode(\"regression\") %>% "
[20] " set_engine(\"xgboost\") "
[21] ""
[22] "test_config_11_dummies_workflow <- "
[23] " workflow() %>% "
[24] " add_recipe(test_config_11_dummies_recipe) %>% "
[25] " add_model(test_config_11_dummies_spec) "
[26] ""
[27] "set.seed(27246)"
[28] "test_config_11_dummies_tune <-"
[29] " tune_grid(test_config_11_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[30] " 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_11_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. However, for this model, binary indicator variables can be "
[11] " ## made for each of the levels of the factors (known as 'one-hot "
[12] " ## encoding'). "
[13] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[14] " step_zv(all_predictors()) "
[15] ""
[16] "test_config_11_no_dummies_spec <- "
[17] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[18] " loss_reduction = tune(), sample_size = tune()) %>% "
[19] " set_mode(\"classification\") %>% "
[20] " set_engine(\"xgboost\") "
[21] ""
[22] "test_config_11_no_dummies_workflow <- "
[23] " workflow() %>% "
[24] " add_recipe(test_config_11_no_dummies_recipe) %>% "
[25] " add_model(test_config_11_no_dummies_spec) "
[26] ""
[27] "set.seed(27246)"
[28] "test_config_11_no_dummies_tune <-"
[29] " tune_grid(test_config_11_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[30] " 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_12_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_12_dummies_spec <- "
[8] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[9] " set_mode(\"regression\") %>% "
[10] " set_engine(\"ranger\") "
[11] ""
[12] "test_config_12_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_12_dummies_recipe) %>% "
[15] " add_model(test_config_12_dummies_spec) "
[16] ""
[17] "set.seed(27246)"
[18] "test_config_12_dummies_tune <-"
[19] " tune_grid(test_config_12_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_12_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_12_no_dummies_spec <- "
[8] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"ranger\") "
[11] ""
[12] "test_config_12_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_12_no_dummies_recipe) %>% "
[15] " add_model(test_config_12_no_dummies_spec) "
[16] ""
[17] "set.seed(27246)"
[18] "test_config_12_no_dummies_tune <-"
[19] " tune_grid(test_config_12_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_13_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Since distance calculations are used, the predictor variables should "
[13] " ## be on the same scale. Before centering and scaling the numeric "
[14] " ## predictors, any predictors with a single unique value are filtered "
[15] " ## out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_13_dummies_spec <- "
[20] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[21] " set_mode(\"regression\") %>% "
[22] " set_engine(\"kknn\") "
[23] ""
[24] "test_config_13_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_13_dummies_recipe) %>% "
[27] " add_model(test_config_13_dummies_spec) "
[28] ""
[29] "set.seed(27246)"
[30] "test_config_13_dummies_tune <-"
[31] " tune_grid(test_config_13_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[32] " 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_13_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Since distance calculations are used, the predictor variables should "
[13] " ## be on the same scale. Before centering and scaling the numeric "
[14] " ## predictors, any predictors with a single unique value are filtered "
[15] " ## out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_13_no_dummies_spec <- "
[20] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[21] " set_mode(\"classification\") %>% "
[22] " set_engine(\"kknn\") "
[23] ""
[24] "test_config_13_no_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_13_no_dummies_recipe) %>% "
[27] " add_model(test_config_13_no_dummies_spec) "
[28] ""
[29] "set.seed(27246)"
[30] "test_config_13_no_dummies_tune <-"
[31] " tune_grid(test_config_13_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[32] " 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_14_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " step_zv(all_predictors()) "
[13] ""
[14] "test_config_14_dummies_spec <- "
[15] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"earth\") "
[18] ""
[19] "test_config_14_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_14_dummies_recipe) %>% "
[22] " add_model(test_config_14_dummies_spec) "
[23] ""
[24] "test_config_14_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[25] ""
[26] "test_config_14_dummies_tune <- "
[27] " tune_grid(test_config_14_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[28] " grid = test_config_14_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_14_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " step_zv(all_predictors()) "
[13] ""
[14] "test_config_14_no_dummies_spec <- "
[15] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"earth\") "
[18] ""
[19] "test_config_14_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_14_no_dummies_recipe) %>% "
[22] " add_model(test_config_14_no_dummies_spec) "
[23] ""
[24] "test_config_14_no_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[25] ""
[26] "test_config_14_no_dummies_tune <- "
[27] " tune_grid(test_config_14_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[28] " grid = test_config_14_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_15_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[5] " ## For modeling, it is preferred to encode qualitative data as factors "
[6] " ## (instead of character). "
[7] " step_string2factor(one_of(\"island\")) %>% "
[8] " step_zv(all_predictors()) "
[9] ""
[10] "test_config_15_dummies_spec <- "
[11] " cubist_rules(committees = tune(), neighbors = tune()) %>% "
[12] " set_engine(\"Cubist\") "
[13] ""
[14] "test_config_15_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_15_dummies_recipe) %>% "
[17] " add_model(test_config_15_dummies_spec) "
[18] ""
[19] "test_config_15_dummies_grid <- tidyr::crossing(committees = c(1:9, (1:5) * "
[20] " 10), neighbors = c(0, 3, 6, 9)) "
[21] ""
[22] "test_config_15_dummies_tune <- "
[23] " tune_grid(test_config_15_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = test_config_15_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_16_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[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_16_dummies_spec <- "
[11] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_16_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_16_dummies_recipe) %>% "
[17] " add_model(test_config_16_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_16_dummies_tune <-"
[21] " tune_grid(test_config_16_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_16_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[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_16_no_dummies_spec <- "
[11] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_16_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_16_no_dummies_recipe) %>% "
[17] " add_model(test_config_16_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_16_no_dummies_tune <-"
[21] " tune_grid(test_config_16_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_17_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[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_17_dummies_spec <- "
[11] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_17_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_17_dummies_recipe) %>% "
[17] " add_model(test_config_17_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_17_dummies_tune <-"
[21] " tune_grid(test_config_17_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_17_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[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_17_no_dummies_spec <- "
[11] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_17_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_17_no_dummies_recipe) %>% "
[17] " add_model(test_config_17_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_17_no_dummies_tune <-"
[21] " tune_grid(test_config_17_no_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_18_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_18_no_dummies_spec <- "
[8] " boost_tree(trees = tune(), min_n = tune()) %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"C5.0\") "
[11] ""
[12] "test_config_18_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_18_no_dummies_recipe) %>% "
[15] " add_model(test_config_18_no_dummies_spec) "
[16] ""
[17] "set.seed(27246)"
[18] "test_config_18_no_dummies_tune <-"
[19] " tune_grid(test_config_18_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_19_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_19_dummies_spec <- "
[10] " linear_reg() %>% "
[11] " set_mode(\"regression\") %>% "
[12] " set_engine(\"glmnet\") "
[13] ""
[14] "test_config_19_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_19_dummies_recipe) %>% "
[17] " add_model(test_config_19_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_19_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_19_no_dummies_spec <- "
[10] " multinom_reg() %>% "
[11] " set_mode(\"classification\") %>% "
[12] " set_engine(\"glmnet\") "
[13] ""
[14] "test_config_19_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_19_no_dummies_recipe) %>% "
[17] " add_model(test_config_19_no_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_20_dummies_spec <- "
[9] " boost_tree() %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"xgboost\") "
[12] ""
[13] "test_config_20_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_20_dummies_recipe) %>% "
[16] " add_model(test_config_20_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_20_no_dummies_spec <- "
[9] " boost_tree() %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"xgboost\") "
[12] ""
[13] "test_config_20_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_20_no_dummies_recipe) %>% "
[16] " add_model(test_config_20_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_21_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_21_dummies_spec <- "
[6] " rand_forest(trees = 1000) %>% "
[7] " set_mode(\"regression\") %>% "
[8] " set_engine(\"ranger\") "
[9] ""
[10] "test_config_21_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_21_dummies_recipe) %>% "
[13] " add_model(test_config_21_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_21_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_21_no_dummies_spec <- "
[6] " rand_forest(trees = 1000) %>% "
[7] " set_mode(\"classification\") %>% "
[8] " set_engine(\"ranger\") "
[9] ""
[10] "test_config_21_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_21_no_dummies_recipe) %>% "
[13] " add_model(test_config_21_no_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_22_dummies_spec <- "
[10] " nearest_neighbor() %>% "
[11] " set_mode(\"regression\") %>% "
[12] " set_engine(\"kknn\") "
[13] ""
[14] "test_config_22_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_22_dummies_recipe) %>% "
[17] " add_model(test_config_22_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_22_no_dummies_spec <- "
[10] " nearest_neighbor() %>% "
[11] " set_mode(\"classification\") %>% "
[12] " set_engine(\"kknn\") "
[13] ""
[14] "test_config_22_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_22_no_dummies_recipe) %>% "
[17] " add_model(test_config_22_no_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_23_dummies_spec <- "
[9] " mars() %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"earth\") "
[12] ""
[13] "test_config_23_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_23_dummies_recipe) %>% "
[16] " add_model(test_config_23_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_23_no_dummies_spec <- "
[9] " mars() %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"earth\") "
[12] ""
[13] "test_config_23_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_23_no_dummies_recipe) %>% "
[16] " add_model(test_config_23_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_24_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_24_dummies_spec <- "
[9] " cubist_rules() %>% "
[10] " set_engine(\"Cubist\") "
[11] ""
[12] "test_config_24_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_24_dummies_recipe) %>% "
[15] " add_model(test_config_24_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_25_dummies_spec <- "
[7] " svm_poly() %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_25_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_25_dummies_recipe) %>% "
[13] " add_model(test_config_25_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_25_no_dummies_spec <- "
[7] " svm_poly() %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_25_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_25_no_dummies_recipe) %>% "
[13] " add_model(test_config_25_no_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_26_dummies_spec <- "
[7] " svm_rbf() %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_26_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_26_dummies_recipe) %>% "
[13] " add_model(test_config_26_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_26_no_dummies_spec <- "
[7] " svm_rbf() %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_26_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_26_no_dummies_recipe) %>% "
[13] " add_model(test_config_26_no_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_27_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_27_no_dummies_spec <- "
[6] " boost_tree() %>% "
[7] " set_mode(\"classification\") %>% "
[8] " set_engine(\"C5.0\") "
[9] ""
[10] "test_config_27_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_27_no_dummies_recipe) %>% "
[13] " add_model(test_config_27_no_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_28_dummies_spec <- "
[10] " linear_reg(penalty = tune(), mixture = tune()) %>% "
[11] " set_mode(\"regression\") %>% "
[12] " set_engine(\"glmnet\") "
[13] ""
[14] "test_config_28_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_28_dummies_recipe) %>% "
[17] " add_model(test_config_28_dummies_spec) "
[18] ""
[19] "test_config_28_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20), "
[20] " mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[21] ""
[22] "test_config_28_dummies_tune <- "
[23] " tune_grid(test_config_28_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = test_config_28_dummies_grid) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_28_no_dummies_spec <- "
[10] " multinom_reg(penalty = tune(), mixture = tune()) %>% "
[11] " set_mode(\"classification\") %>% "
[12] " set_engine(\"glmnet\") "
[13] ""
[14] "test_config_28_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_28_no_dummies_recipe) %>% "
[17] " add_model(test_config_28_no_dummies_spec) "
[18] ""
[19] "test_config_28_no_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, "
[20] " length.out = 20), mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[21] ""
[22] "test_config_28_no_dummies_tune <- "
[23] " tune_grid(test_config_28_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = test_config_28_no_dummies_grid) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_29_dummies_spec <- "
[9] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[10] " loss_reduction = tune(), sample_size = tune()) %>% "
[11] " set_mode(\"regression\") %>% "
[12] " set_engine(\"xgboost\") "
[13] ""
[14] "test_config_29_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_29_dummies_recipe) %>% "
[17] " add_model(test_config_29_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_29_dummies_tune <-"
[21] " tune_grid(test_config_29_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_29_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_29_no_dummies_spec <- "
[9] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[10] " loss_reduction = tune(), sample_size = tune()) %>% "
[11] " set_mode(\"classification\") %>% "
[12] " set_engine(\"xgboost\") "
[13] ""
[14] "test_config_29_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_29_no_dummies_recipe) %>% "
[17] " add_model(test_config_29_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_29_no_dummies_tune <-"
[21] " tune_grid(test_config_29_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_30_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_30_dummies_spec <- "
[6] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[7] " set_mode(\"regression\") %>% "
[8] " set_engine(\"ranger\") "
[9] ""
[10] "test_config_30_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_30_dummies_recipe) %>% "
[13] " add_model(test_config_30_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_30_dummies_tune <-"
[17] " tune_grid(test_config_30_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_30_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_30_no_dummies_spec <- "
[6] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[7] " set_mode(\"classification\") %>% "
[8] " set_engine(\"ranger\") "
[9] ""
[10] "test_config_30_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_30_no_dummies_recipe) %>% "
[13] " add_model(test_config_30_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_30_no_dummies_tune <-"
[17] " tune_grid(test_config_30_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_31_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_31_dummies_spec <- "
[10] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[11] " set_mode(\"regression\") %>% "
[12] " set_engine(\"kknn\") "
[13] ""
[14] "test_config_31_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_31_dummies_recipe) %>% "
[17] " add_model(test_config_31_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_31_dummies_tune <-"
[21] " tune_grid(test_config_31_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_31_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_31_no_dummies_spec <- "
[10] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[11] " set_mode(\"classification\") %>% "
[12] " set_engine(\"kknn\") "
[13] ""
[14] "test_config_31_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_31_no_dummies_recipe) %>% "
[17] " add_model(test_config_31_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_31_no_dummies_tune <-"
[21] " tune_grid(test_config_31_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_32_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_32_dummies_spec <- "
[9] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"earth\") "
[12] ""
[13] "test_config_32_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_32_dummies_recipe) %>% "
[16] " add_model(test_config_32_dummies_spec) "
[17] ""
[18] "test_config_32_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[19] ""
[20] "test_config_32_dummies_tune <- "
[21] " tune_grid(test_config_32_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = test_config_32_dummies_grid) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_32_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_32_no_dummies_spec <- "
[9] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"earth\") "
[12] ""
[13] "test_config_32_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_32_no_dummies_recipe) %>% "
[16] " add_model(test_config_32_no_dummies_spec) "
[17] ""
[18] "test_config_32_no_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[19] ""
[20] "test_config_32_no_dummies_tune <- "
[21] " tune_grid(test_config_32_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = test_config_32_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_33_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_33_dummies_spec <- "
[9] " cubist_rules(committees = tune(), neighbors = tune()) %>% "
[10] " set_engine(\"Cubist\") "
[11] ""
[12] "test_config_33_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_33_dummies_recipe) %>% "
[15] " add_model(test_config_33_dummies_spec) "
[16] ""
[17] "test_config_33_dummies_grid <- tidyr::crossing(committees = c(1:9, (1:5) * "
[18] " 10), neighbors = c(0, 3, 6, 9)) "
[19] ""
[20] "test_config_33_dummies_tune <- "
[21] " tune_grid(test_config_33_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = test_config_33_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_34_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_34_dummies_spec <- "
[7] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_34_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_34_dummies_recipe) %>% "
[13] " add_model(test_config_34_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_34_dummies_tune <-"
[17] " tune_grid(test_config_34_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_34_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_34_no_dummies_spec <- "
[7] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_34_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_34_no_dummies_recipe) %>% "
[13] " add_model(test_config_34_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_34_no_dummies_tune <-"
[17] " tune_grid(test_config_34_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_35_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_35_dummies_spec <- "
[7] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_35_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_35_dummies_recipe) %>% "
[13] " add_model(test_config_35_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_35_dummies_tune <-"
[17] " tune_grid(test_config_35_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_35_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_35_no_dummies_spec <- "
[7] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_35_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_35_no_dummies_recipe) %>% "
[13] " add_model(test_config_35_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_35_no_dummies_tune <-"
[17] " tune_grid(test_config_35_no_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_36_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_36_no_dummies_spec <- "
[6] " boost_tree(trees = tune(), min_n = tune()) %>% "
[7] " set_mode(\"classification\") %>% "
[8] " set_engine(\"C5.0\") "
[9] ""
[10] "test_config_36_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_36_no_dummies_recipe) %>% "
[13] " add_model(test_config_36_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_36_no_dummies_tune <-"
[17] " tune_grid(test_config_36_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[18] " grid = stop(\"add number of candidate points\"))"
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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] "test_config_1_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Regularization methods sum up functions of the model slope "
[13] " ## coefficients. Because of this, the predictor variables should be on "
[14] " ## the same scale. Before centering and scaling the numeric predictors, "
[15] " ## any predictors with a single unique value are filtered out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_1_dummies_spec <- "
[20] " linear_reg() %>% "
[21] " set_mode(\"regression\") %>% "
[22] " set_engine(\"glmnet\") "
[23] ""
[24] "test_config_1_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_1_dummies_recipe) %>% "
[27] " 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] "test_config_1_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Regularization methods sum up functions of the model slope "
[13] " ## coefficients. Because of this, the predictor variables should be on "
[14] " ## the same scale. Before centering and scaling the numeric predictors, "
[15] " ## any predictors with a single unique value are filtered out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_1_no_dummies_spec <- "
[20] " multinom_reg() %>% "
[21] " set_mode(\"classification\") %>% "
[22] " set_engine(\"glmnet\") "
[23] ""
[24] "test_config_1_no_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_1_no_dummies_recipe) %>% "
[27] " add_model(test_config_1_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_2_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. However, for this model, binary indicator variables can be "
[11] " ## made for each of the levels of the factors (known as 'one-hot "
[12] " ## encoding'). "
[13] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[14] " step_zv(all_predictors()) "
[15] ""
[16] "test_config_2_dummies_spec <- "
[17] " boost_tree() %>% "
[18] " set_mode(\"regression\") %>% "
[19] " set_engine(\"xgboost\") "
[20] ""
[21] "test_config_2_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_2_dummies_recipe) %>% "
[24] " add_model(test_config_2_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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. However, for this model, binary indicator variables can be "
[11] " ## made for each of the levels of the factors (known as 'one-hot "
[12] " ## encoding'). "
[13] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[14] " step_zv(all_predictors()) "
[15] ""
[16] "test_config_2_no_dummies_spec <- "
[17] " boost_tree() %>% "
[18] " set_mode(\"classification\") %>% "
[19] " set_engine(\"xgboost\") "
[20] ""
[21] "test_config_2_no_dummies_workflow <- "
[22] " workflow() %>% "
[23] " add_recipe(test_config_2_no_dummies_recipe) %>% "
[24] " 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] "test_config_3_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_3_dummies_spec <- "
[8] " rand_forest(trees = 1000) %>% "
[9] " set_mode(\"regression\") %>% "
[10] " set_engine(\"ranger\") "
[11] ""
[12] "test_config_3_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_3_dummies_recipe) %>% "
[15] " add_model(test_config_3_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_3_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_3_no_dummies_spec <- "
[8] " rand_forest(trees = 1000) %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"ranger\") "
[11] ""
[12] "test_config_3_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_3_no_dummies_recipe) %>% "
[15] " add_model(test_config_3_no_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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Since distance calculations are used, the predictor variables should "
[13] " ## be on the same scale. Before centering and scaling the numeric "
[14] " ## predictors, any predictors with a single unique value are filtered "
[15] " ## out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_4_dummies_spec <- "
[20] " nearest_neighbor() %>% "
[21] " set_mode(\"regression\") %>% "
[22] " set_engine(\"kknn\") "
[23] ""
[24] "test_config_4_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_4_dummies_recipe) %>% "
[27] " 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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Since distance calculations are used, the predictor variables should "
[13] " ## be on the same scale. Before centering and scaling the numeric "
[14] " ## predictors, any predictors with a single unique value are filtered "
[15] " ## out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_4_no_dummies_spec <- "
[20] " nearest_neighbor() %>% "
[21] " set_mode(\"classification\") %>% "
[22] " set_engine(\"kknn\") "
[23] ""
[24] "test_config_4_no_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_4_no_dummies_recipe) %>% "
[27] " 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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " step_zv(all_predictors()) "
[13] ""
[14] "test_config_5_dummies_spec <- "
[15] " mars() %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"earth\") "
[18] ""
[19] "test_config_5_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_5_dummies_recipe) %>% "
[22] " 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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " step_zv(all_predictors()) "
[13] ""
[14] "test_config_5_no_dummies_spec <- "
[15] " mars() %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"earth\") "
[18] ""
[19] "test_config_5_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_5_no_dummies_recipe) %>% "
[22] " 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] "library(rules)"
[2] ""
[3] "test_config_6_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[5] " ## For modeling, it is preferred to encode qualitative data as factors "
[6] " ## (instead of character). "
[7] " step_string2factor(one_of(\"island\")) %>% "
[8] " step_zv(all_predictors()) "
[9] ""
[10] "test_config_6_dummies_spec <- "
[11] " cubist_rules() %>% "
[12] " set_engine(\"Cubist\") "
[13] ""
[14] "test_config_6_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_6_dummies_recipe) %>% "
[17] " add_model(test_config_6_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, clipboard = TRUE) %>% "
[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, clipboard = TRUE) %>% "
[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, clipboard = TRUE) %>% "
[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, clipboard = TRUE) %>% "
[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
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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_9_no_dummies_spec <- "
[8] " boost_tree() %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"C5.0\") "
[11] ""
[12] "test_config_9_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_9_no_dummies_recipe) %>% "
[15] " 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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Regularization methods sum up functions of the model slope "
[13] " ## coefficients. Because of this, the predictor variables should be on "
[14] " ## the same scale. Before centering and scaling the numeric predictors, "
[15] " ## any predictors with a single unique value are filtered out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_10_dummies_spec <- "
[20] " linear_reg(penalty = tune(), mixture = tune()) %>% "
[21] " set_mode(\"regression\") %>% "
[22] " set_engine(\"glmnet\") "
[23] ""
[24] "test_config_10_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_10_dummies_recipe) %>% "
[27] " add_model(test_config_10_dummies_spec) "
[28] ""
[29] "test_config_10_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20), "
[30] " mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[31] ""
[32] "test_config_10_dummies_tune <- "
[33] " tune_grid(test_config_10_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[34] " grid = test_config_10_dummies_grid) "
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, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Regularization methods sum up functions of the model slope "
[13] " ## coefficients. Because of this, the predictor variables should be on "
[14] " ## the same scale. Before centering and scaling the numeric predictors, "
[15] " ## any predictors with a single unique value are filtered out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_10_no_dummies_spec <- "
[20] " multinom_reg(penalty = tune(), mixture = tune()) %>% "
[21] " set_mode(\"classification\") %>% "
[22] " set_engine(\"glmnet\") "
[23] ""
[24] "test_config_10_no_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_10_no_dummies_recipe) %>% "
[27] " add_model(test_config_10_no_dummies_spec) "
[28] ""
[29] "test_config_10_no_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, "
[30] " length.out = 20), mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[31] ""
[32] "test_config_10_no_dummies_tune <- "
[33] " tune_grid(test_config_10_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[34] " grid = test_config_10_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_11_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. However, for this model, binary indicator variables can be "
[11] " ## made for each of the levels of the factors (known as 'one-hot "
[12] " ## encoding'). "
[13] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[14] " step_zv(all_predictors()) "
[15] ""
[16] "test_config_11_dummies_spec <- "
[17] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[18] " loss_reduction = tune(), sample_size = tune()) %>% "
[19] " set_mode(\"regression\") %>% "
[20] " set_engine(\"xgboost\") "
[21] ""
[22] "test_config_11_dummies_workflow <- "
[23] " workflow() %>% "
[24] " add_recipe(test_config_11_dummies_recipe) %>% "
[25] " add_model(test_config_11_dummies_spec) "
[26] ""
[27] "set.seed(27246)"
[28] "test_config_11_dummies_tune <-"
[29] " tune_grid(test_config_11_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[30] " 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_11_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. However, for this model, binary indicator variables can be "
[11] " ## made for each of the levels of the factors (known as 'one-hot "
[12] " ## encoding'). "
[13] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[14] " step_zv(all_predictors()) "
[15] ""
[16] "test_config_11_no_dummies_spec <- "
[17] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[18] " loss_reduction = tune(), sample_size = tune()) %>% "
[19] " set_mode(\"classification\") %>% "
[20] " set_engine(\"xgboost\") "
[21] ""
[22] "test_config_11_no_dummies_workflow <- "
[23] " workflow() %>% "
[24] " add_recipe(test_config_11_no_dummies_recipe) %>% "
[25] " add_model(test_config_11_no_dummies_spec) "
[26] ""
[27] "set.seed(27246)"
[28] "test_config_11_no_dummies_tune <-"
[29] " tune_grid(test_config_11_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[30] " 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_12_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_12_dummies_spec <- "
[8] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[9] " set_mode(\"regression\") %>% "
[10] " set_engine(\"ranger\") "
[11] ""
[12] "test_config_12_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_12_dummies_recipe) %>% "
[15] " add_model(test_config_12_dummies_spec) "
[16] ""
[17] "set.seed(27246)"
[18] "test_config_12_dummies_tune <-"
[19] " tune_grid(test_config_12_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_12_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_12_no_dummies_spec <- "
[8] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"ranger\") "
[11] ""
[12] "test_config_12_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_12_no_dummies_recipe) %>% "
[15] " add_model(test_config_12_no_dummies_spec) "
[16] ""
[17] "set.seed(27246)"
[18] "test_config_12_no_dummies_tune <-"
[19] " tune_grid(test_config_12_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_13_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Since distance calculations are used, the predictor variables should "
[13] " ## be on the same scale. Before centering and scaling the numeric "
[14] " ## predictors, any predictors with a single unique value are filtered "
[15] " ## out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_13_dummies_spec <- "
[20] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[21] " set_mode(\"regression\") %>% "
[22] " set_engine(\"kknn\") "
[23] ""
[24] "test_config_13_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_13_dummies_recipe) %>% "
[27] " add_model(test_config_13_dummies_spec) "
[28] ""
[29] "set.seed(27246)"
[30] "test_config_13_dummies_tune <-"
[31] " tune_grid(test_config_13_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[32] " 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_13_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " ## Since distance calculations are used, the predictor variables should "
[13] " ## be on the same scale. Before centering and scaling the numeric "
[14] " ## predictors, any predictors with a single unique value are filtered "
[15] " ## out. "
[16] " step_zv(all_predictors()) %>% "
[17] " step_normalize(all_numeric_predictors()) "
[18] ""
[19] "test_config_13_no_dummies_spec <- "
[20] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[21] " set_mode(\"classification\") %>% "
[22] " set_engine(\"kknn\") "
[23] ""
[24] "test_config_13_no_dummies_workflow <- "
[25] " workflow() %>% "
[26] " add_recipe(test_config_13_no_dummies_recipe) %>% "
[27] " add_model(test_config_13_no_dummies_spec) "
[28] ""
[29] "set.seed(27246)"
[30] "test_config_13_no_dummies_tune <-"
[31] " tune_grid(test_config_13_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[32] " 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_14_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " step_zv(all_predictors()) "
[13] ""
[14] "test_config_14_dummies_spec <- "
[15] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[16] " set_mode(\"regression\") %>% "
[17] " set_engine(\"earth\") "
[18] ""
[19] "test_config_14_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_14_dummies_recipe) %>% "
[22] " add_model(test_config_14_dummies_spec) "
[23] ""
[24] "test_config_14_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[25] ""
[26] "test_config_14_dummies_tune <- "
[27] " tune_grid(test_config_14_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[28] " grid = test_config_14_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_14_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_novel(all_nominal_predictors()) %>% "
[7] " ## This model requires the predictors to be numeric. The most common "
[8] " ## method to convert qualitative predictors to numeric is to create "
[9] " ## binary indicator variables (aka dummy variables) from these "
[10] " ## predictors. "
[11] " step_dummy(all_nominal_predictors()) %>% "
[12] " step_zv(all_predictors()) "
[13] ""
[14] "test_config_14_no_dummies_spec <- "
[15] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[16] " set_mode(\"classification\") %>% "
[17] " set_engine(\"earth\") "
[18] ""
[19] "test_config_14_no_dummies_workflow <- "
[20] " workflow() %>% "
[21] " add_recipe(test_config_14_no_dummies_recipe) %>% "
[22] " add_model(test_config_14_no_dummies_spec) "
[23] ""
[24] "test_config_14_no_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[25] ""
[26] "test_config_14_no_dummies_tune <- "
[27] " tune_grid(test_config_14_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[28] " grid = test_config_14_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_15_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[5] " ## For modeling, it is preferred to encode qualitative data as factors "
[6] " ## (instead of character). "
[7] " step_string2factor(one_of(\"island\")) %>% "
[8] " step_zv(all_predictors()) "
[9] ""
[10] "test_config_15_dummies_spec <- "
[11] " cubist_rules(committees = tune(), neighbors = tune()) %>% "
[12] " set_engine(\"Cubist\") "
[13] ""
[14] "test_config_15_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_15_dummies_recipe) %>% "
[17] " add_model(test_config_15_dummies_spec) "
[18] ""
[19] "test_config_15_dummies_grid <- tidyr::crossing(committees = c(1:9, (1:5) * "
[20] " 10), neighbors = c(0, 3, 6, 9)) "
[21] ""
[22] "test_config_15_dummies_tune <- "
[23] " tune_grid(test_config_15_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = test_config_15_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_16_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[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_16_dummies_spec <- "
[11] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_16_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_16_dummies_recipe) %>% "
[17] " add_model(test_config_16_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_16_dummies_tune <-"
[21] " tune_grid(test_config_16_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_16_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[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_16_no_dummies_spec <- "
[11] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_16_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_16_no_dummies_recipe) %>% "
[17] " add_model(test_config_16_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_16_no_dummies_tune <-"
[21] " tune_grid(test_config_16_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_17_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[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_17_dummies_spec <- "
[11] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[12] " set_mode(\"regression\") "
[13] ""
[14] "test_config_17_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_17_dummies_recipe) %>% "
[17] " add_model(test_config_17_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_17_dummies_tune <-"
[21] " tune_grid(test_config_17_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_17_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[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_17_no_dummies_spec <- "
[11] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[12] " set_mode(\"classification\") "
[13] ""
[14] "test_config_17_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_17_no_dummies_recipe) %>% "
[17] " add_model(test_config_17_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_17_no_dummies_tune <-"
[21] " tune_grid(test_config_17_no_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_18_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " ## For modeling, it is preferred to encode qualitative data as factors "
[4] " ## (instead of character). "
[5] " step_string2factor(one_of(\"island\")) "
[6] ""
[7] "test_config_18_no_dummies_spec <- "
[8] " boost_tree(trees = tune(), min_n = tune()) %>% "
[9] " set_mode(\"classification\") %>% "
[10] " set_engine(\"C5.0\") "
[11] ""
[12] "test_config_18_no_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_18_no_dummies_recipe) %>% "
[15] " add_model(test_config_18_no_dummies_spec) "
[16] ""
[17] "set.seed(27246)"
[18] "test_config_18_no_dummies_tune <-"
[19] " tune_grid(test_config_18_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_19_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_19_dummies_spec <- "
[10] " linear_reg() %>% "
[11] " set_mode(\"regression\") %>% "
[12] " set_engine(\"glmnet\") "
[13] ""
[14] "test_config_19_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_19_dummies_recipe) %>% "
[17] " add_model(test_config_19_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_19_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_19_no_dummies_spec <- "
[10] " multinom_reg() %>% "
[11] " set_mode(\"classification\") %>% "
[12] " set_engine(\"glmnet\") "
[13] ""
[14] "test_config_19_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_19_no_dummies_recipe) %>% "
[17] " add_model(test_config_19_no_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_20_dummies_spec <- "
[9] " boost_tree() %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"xgboost\") "
[12] ""
[13] "test_config_20_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_20_dummies_recipe) %>% "
[16] " add_model(test_config_20_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_20_no_dummies_spec <- "
[9] " boost_tree() %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"xgboost\") "
[12] ""
[13] "test_config_20_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_20_no_dummies_recipe) %>% "
[16] " add_model(test_config_20_no_dummies_spec) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_21_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_21_dummies_spec <- "
[6] " rand_forest(trees = 1000) %>% "
[7] " set_mode(\"regression\") %>% "
[8] " set_engine(\"ranger\") "
[9] ""
[10] "test_config_21_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_21_dummies_recipe) %>% "
[13] " add_model(test_config_21_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_21_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_21_no_dummies_spec <- "
[6] " rand_forest(trees = 1000) %>% "
[7] " set_mode(\"classification\") %>% "
[8] " set_engine(\"ranger\") "
[9] ""
[10] "test_config_21_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_21_no_dummies_recipe) %>% "
[13] " add_model(test_config_21_no_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_22_dummies_spec <- "
[10] " nearest_neighbor() %>% "
[11] " set_mode(\"regression\") %>% "
[12] " set_engine(\"kknn\") "
[13] ""
[14] "test_config_22_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_22_dummies_recipe) %>% "
[17] " add_model(test_config_22_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_22_no_dummies_spec <- "
[10] " nearest_neighbor() %>% "
[11] " set_mode(\"classification\") %>% "
[12] " set_engine(\"kknn\") "
[13] ""
[14] "test_config_22_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_22_no_dummies_recipe) %>% "
[17] " add_model(test_config_22_no_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_23_dummies_spec <- "
[9] " mars() %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"earth\") "
[12] ""
[13] "test_config_23_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_23_dummies_recipe) %>% "
[16] " add_model(test_config_23_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_23_no_dummies_spec <- "
[9] " mars() %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"earth\") "
[12] ""
[13] "test_config_23_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_23_no_dummies_recipe) %>% "
[16] " add_model(test_config_23_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_24_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_24_dummies_spec <- "
[9] " cubist_rules() %>% "
[10] " set_engine(\"Cubist\") "
[11] ""
[12] "test_config_24_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_24_dummies_recipe) %>% "
[15] " add_model(test_config_24_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_25_dummies_spec <- "
[7] " svm_poly() %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_25_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_25_dummies_recipe) %>% "
[13] " add_model(test_config_25_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_25_no_dummies_spec <- "
[7] " svm_poly() %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_25_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_25_no_dummies_recipe) %>% "
[13] " add_model(test_config_25_no_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_26_dummies_spec <- "
[7] " svm_rbf() %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_26_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_26_dummies_recipe) %>% "
[13] " add_model(test_config_26_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_26_no_dummies_spec <- "
[7] " svm_rbf() %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_26_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_26_no_dummies_recipe) %>% "
[13] " add_model(test_config_26_no_dummies_spec) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_27_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_27_no_dummies_spec <- "
[6] " boost_tree() %>% "
[7] " set_mode(\"classification\") %>% "
[8] " set_engine(\"C5.0\") "
[9] ""
[10] "test_config_27_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_27_no_dummies_recipe) %>% "
[13] " add_model(test_config_27_no_dummies_spec) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_28_dummies_spec <- "
[10] " linear_reg(penalty = tune(), mixture = tune()) %>% "
[11] " set_mode(\"regression\") %>% "
[12] " set_engine(\"glmnet\") "
[13] ""
[14] "test_config_28_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_28_dummies_recipe) %>% "
[17] " add_model(test_config_28_dummies_spec) "
[18] ""
[19] "test_config_28_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20), "
[20] " mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[21] ""
[22] "test_config_28_dummies_tune <- "
[23] " tune_grid(test_config_28_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = test_config_28_dummies_grid) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_28_no_dummies_spec <- "
[10] " multinom_reg(penalty = tune(), mixture = tune()) %>% "
[11] " set_mode(\"classification\") %>% "
[12] " set_engine(\"glmnet\") "
[13] ""
[14] "test_config_28_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_28_no_dummies_recipe) %>% "
[17] " add_model(test_config_28_no_dummies_spec) "
[18] ""
[19] "test_config_28_no_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, "
[20] " length.out = 20), mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1)) "
[21] ""
[22] "test_config_28_no_dummies_tune <- "
[23] " tune_grid(test_config_28_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[24] " grid = test_config_28_no_dummies_grid) "
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, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_29_dummies_spec <- "
[9] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[10] " loss_reduction = tune(), sample_size = tune()) %>% "
[11] " set_mode(\"regression\") %>% "
[12] " set_engine(\"xgboost\") "
[13] ""
[14] "test_config_29_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_29_dummies_recipe) %>% "
[17] " add_model(test_config_29_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_29_dummies_tune <-"
[21] " tune_grid(test_config_29_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_29_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_29_no_dummies_spec <- "
[9] " boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), "
[10] " loss_reduction = tune(), sample_size = tune()) %>% "
[11] " set_mode(\"classification\") %>% "
[12] " set_engine(\"xgboost\") "
[13] ""
[14] "test_config_29_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_29_no_dummies_recipe) %>% "
[17] " add_model(test_config_29_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_29_no_dummies_tune <-"
[21] " tune_grid(test_config_29_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_30_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_30_dummies_spec <- "
[6] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[7] " set_mode(\"regression\") %>% "
[8] " set_engine(\"ranger\") "
[9] ""
[10] "test_config_30_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_30_dummies_recipe) %>% "
[13] " add_model(test_config_30_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_30_dummies_tune <-"
[17] " tune_grid(test_config_30_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_30_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_30_no_dummies_spec <- "
[6] " rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% "
[7] " set_mode(\"classification\") %>% "
[8] " set_engine(\"ranger\") "
[9] ""
[10] "test_config_30_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_30_no_dummies_recipe) %>% "
[13] " add_model(test_config_30_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_30_no_dummies_tune <-"
[17] " tune_grid(test_config_30_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_31_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_31_dummies_spec <- "
[10] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[11] " set_mode(\"regression\") %>% "
[12] " set_engine(\"kknn\") "
[13] ""
[14] "test_config_31_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_31_dummies_recipe) %>% "
[17] " add_model(test_config_31_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_31_dummies_tune <-"
[21] " tune_grid(test_config_31_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_31_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) %>% "
[7] " step_normalize(all_numeric_predictors()) "
[8] ""
[9] "test_config_31_no_dummies_spec <- "
[10] " nearest_neighbor(neighbors = tune(), weight_func = tune()) %>% "
[11] " set_mode(\"classification\") %>% "
[12] " set_engine(\"kknn\") "
[13] ""
[14] "test_config_31_no_dummies_workflow <- "
[15] " workflow() %>% "
[16] " add_recipe(test_config_31_no_dummies_recipe) %>% "
[17] " add_model(test_config_31_no_dummies_spec) "
[18] ""
[19] "set.seed(27246)"
[20] "test_config_31_no_dummies_tune <-"
[21] " tune_grid(test_config_31_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_32_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_32_dummies_spec <- "
[9] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[10] " set_mode(\"regression\") %>% "
[11] " set_engine(\"earth\") "
[12] ""
[13] "test_config_32_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_32_dummies_recipe) %>% "
[16] " add_model(test_config_32_dummies_spec) "
[17] ""
[18] "test_config_32_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[19] ""
[20] "test_config_32_dummies_tune <- "
[21] " tune_grid(test_config_32_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = test_config_32_dummies_grid) "
Code
no_dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_32_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) %>% "
[4] " step_novel(all_nominal_predictors()) %>% "
[5] " step_dummy(all_nominal_predictors()) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_32_no_dummies_spec <- "
[9] " mars(num_terms = tune(), prod_degree = tune(), prune_method = \"none\") %>% "
[10] " set_mode(\"classification\") %>% "
[11] " set_engine(\"earth\") "
[12] ""
[13] "test_config_32_no_dummies_workflow <- "
[14] " workflow() %>% "
[15] " add_recipe(test_config_32_no_dummies_recipe) %>% "
[16] " add_model(test_config_32_no_dummies_spec) "
[17] ""
[18] "test_config_32_no_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2) "
[19] ""
[20] "test_config_32_no_dummies_tune <- "
[21] " tune_grid(test_config_32_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = test_config_32_no_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "library(rules)"
[2] ""
[3] "test_config_33_dummies_recipe <- "
[4] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[5] " step_string2factor(one_of(\"island\")) %>% "
[6] " step_zv(all_predictors()) "
[7] ""
[8] "test_config_33_dummies_spec <- "
[9] " cubist_rules(committees = tune(), neighbors = tune()) %>% "
[10] " set_engine(\"Cubist\") "
[11] ""
[12] "test_config_33_dummies_workflow <- "
[13] " workflow() %>% "
[14] " add_recipe(test_config_33_dummies_recipe) %>% "
[15] " add_model(test_config_33_dummies_spec) "
[16] ""
[17] "test_config_33_dummies_grid <- tidyr::crossing(committees = c(1:9, (1:5) * "
[18] " 10), neighbors = c(0, 3, 6, 9)) "
[19] ""
[20] "test_config_33_dummies_tune <- "
[21] " tune_grid(test_config_33_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[22] " grid = test_config_33_dummies_grid) "
Code
dummy_clip_template(model, prefix, verbose, tune)
Message <cliMessage>
v code is on the clipboard.
Output
[1] "test_config_34_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_34_dummies_spec <- "
[7] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_34_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_34_dummies_recipe) %>% "
[13] " add_model(test_config_34_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_34_dummies_tune <-"
[17] " tune_grid(test_config_34_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_34_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_34_no_dummies_spec <- "
[7] " svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_34_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_34_no_dummies_recipe) %>% "
[13] " add_model(test_config_34_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_34_no_dummies_tune <-"
[17] " tune_grid(test_config_34_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_35_dummies_recipe <- "
[2] " recipe(formula = body_mass_g ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_35_dummies_spec <- "
[7] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[8] " set_mode(\"regression\") "
[9] ""
[10] "test_config_35_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_35_dummies_recipe) %>% "
[13] " add_model(test_config_35_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_35_dummies_tune <-"
[17] " tune_grid(test_config_35_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_35_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_zv(all_predictors()) %>% "
[4] " step_normalize(all_numeric_predictors()) "
[5] ""
[6] "test_config_35_no_dummies_spec <- "
[7] " svm_rbf(cost = tune(), rbf_sigma = tune()) %>% "
[8] " set_mode(\"classification\") "
[9] ""
[10] "test_config_35_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_35_no_dummies_recipe) %>% "
[13] " add_model(test_config_35_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_35_no_dummies_tune <-"
[17] " tune_grid(test_config_35_no_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_36_no_dummies_recipe <- "
[2] " recipe(formula = species ~ ., data = penguins, clipboard = TRUE) %>% "
[3] " step_string2factor(one_of(\"island\")) "
[4] ""
[5] "test_config_36_no_dummies_spec <- "
[6] " boost_tree(trees = tune(), min_n = tune()) %>% "
[7] " set_mode(\"classification\") %>% "
[8] " set_engine(\"C5.0\") "
[9] ""
[10] "test_config_36_no_dummies_workflow <- "
[11] " workflow() %>% "
[12] " add_recipe(test_config_36_no_dummies_recipe) %>% "
[13] " add_model(test_config_36_no_dummies_spec) "
[14] ""
[15] "set.seed(27246)"
[16] "test_config_36_no_dummies_tune <-"
[17] " tune_grid(test_config_36_no_dummies_workflow, resamples = stop(\"add your rsample object\"), "
[18] " grid = stop(\"add number of candidate points\"))"
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