Nothing
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_1_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
## Regularization methods sum up functions of the model slope
## coefficients. Because of this, the predictor variables should be on
## the same scale. Before centering and scaling the numeric predictors,
## any predictors with a single unique value are filtered out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_1_dummies_spec <-
linear_reg() %>%
set_mode("regression") %>%
set_engine("glmnet")
test_config_1_dummies_workflow <-
workflow() %>%
add_recipe(test_config_1_dummies_recipe) %>%
add_model(test_config_1_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_1_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
## Regularization methods sum up functions of the model slope
## coefficients. Because of this, the predictor variables should be on
## the same scale. Before centering and scaling the numeric predictors,
## any predictors with a single unique value are filtered out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_1_no_dummies_spec <-
multinom_reg() %>%
set_mode("classification") %>%
set_engine("glmnet")
test_config_1_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_1_no_dummies_recipe) %>%
add_model(test_config_1_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_2_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors. However, for this model, binary indicator variables can be
## made for each of the levels of the factors (known as 'one-hot
## encoding').
step_dummy(all_nominal_predictors(), one_hot = TRUE) %>%
step_zv(all_predictors())
test_config_2_dummies_spec <-
boost_tree() %>%
set_mode("regression") %>%
set_engine("xgboost")
test_config_2_dummies_workflow <-
workflow() %>%
add_recipe(test_config_2_dummies_recipe) %>%
add_model(test_config_2_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_2_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors. However, for this model, binary indicator variables can be
## made for each of the levels of the factors (known as 'one-hot
## encoding').
step_dummy(all_nominal_predictors(), one_hot = TRUE) %>%
step_zv(all_predictors())
test_config_2_no_dummies_spec <-
boost_tree() %>%
set_mode("classification") %>%
set_engine("xgboost")
test_config_2_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_2_no_dummies_recipe) %>%
add_model(test_config_2_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_3_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island"))
test_config_3_dummies_spec <-
rand_forest(trees = 1000) %>%
set_mode("regression") %>%
set_engine("ranger")
test_config_3_dummies_workflow <-
workflow() %>%
add_recipe(test_config_3_dummies_recipe) %>%
add_model(test_config_3_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_3_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island"))
test_config_3_no_dummies_spec <-
rand_forest(trees = 1000) %>%
set_mode("classification") %>%
set_engine("ranger")
test_config_3_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_3_no_dummies_recipe) %>%
add_model(test_config_3_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_4_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
## Since distance calculations are used, the predictor variables should
## be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_4_dummies_spec <-
nearest_neighbor() %>%
set_mode("regression") %>%
set_engine("kknn")
test_config_4_dummies_workflow <-
workflow() %>%
add_recipe(test_config_4_dummies_recipe) %>%
add_model(test_config_4_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_4_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
## Since distance calculations are used, the predictor variables should
## be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_4_no_dummies_spec <-
nearest_neighbor() %>%
set_mode("classification") %>%
set_engine("kknn")
test_config_4_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_4_no_dummies_recipe) %>%
add_model(test_config_4_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_5_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors())
test_config_5_dummies_spec <-
mars() %>%
set_mode("regression") %>%
set_engine("earth")
test_config_5_dummies_workflow <-
workflow() %>%
add_recipe(test_config_5_dummies_recipe) %>%
add_model(test_config_5_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_5_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors())
test_config_5_no_dummies_spec <-
mars() %>%
set_mode("classification") %>%
set_engine("earth")
test_config_5_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_5_no_dummies_recipe) %>%
add_model(test_config_5_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
library(rules)
test_config_6_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_zv(all_predictors())
test_config_6_dummies_spec <-
cubist_rules() %>%
set_engine("Cubist")
test_config_6_dummies_workflow <-
workflow() %>%
add_recipe(test_config_6_dummies_recipe) %>%
add_model(test_config_6_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_7_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## Since dot product calculations are used, the predictor variables
## should be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_7_dummies_spec <-
svm_poly() %>%
set_mode("regression")
test_config_7_dummies_workflow <-
workflow() %>%
add_recipe(test_config_7_dummies_recipe) %>%
add_model(test_config_7_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_7_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## Since dot product calculations are used, the predictor variables
## should be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_7_no_dummies_spec <-
svm_poly() %>%
set_mode("classification")
test_config_7_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_7_no_dummies_recipe) %>%
add_model(test_config_7_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_8_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## Since dot product calculations are used, the predictor variables
## should be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_8_dummies_spec <-
svm_rbf() %>%
set_mode("regression")
test_config_8_dummies_workflow <-
workflow() %>%
add_recipe(test_config_8_dummies_recipe) %>%
add_model(test_config_8_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_8_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## Since dot product calculations are used, the predictor variables
## should be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_8_no_dummies_spec <-
svm_rbf() %>%
set_mode("classification")
test_config_8_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_8_no_dummies_recipe) %>%
add_model(test_config_8_no_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_9_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island"))
test_config_9_no_dummies_spec <-
boost_tree() %>%
set_mode("classification") %>%
set_engine("C5.0")
test_config_9_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_9_no_dummies_recipe) %>%
add_model(test_config_9_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_10_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
## Regularization methods sum up functions of the model slope
## coefficients. Because of this, the predictor variables should be on
## the same scale. Before centering and scaling the numeric predictors,
## any predictors with a single unique value are filtered out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_10_dummies_spec <-
linear_reg(penalty = tune(), mixture = tune()) %>%
set_mode("regression") %>%
set_engine("glmnet")
test_config_10_dummies_workflow <-
workflow() %>%
add_recipe(test_config_10_dummies_recipe) %>%
add_model(test_config_10_dummies_spec)
test_config_10_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20),
mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1))
test_config_10_dummies_tune <-
tune_grid(test_config_10_dummies_workflow, resamples = stop("add your rsample object"),
grid = test_config_10_dummies_grid)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_10_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
## Regularization methods sum up functions of the model slope
## coefficients. Because of this, the predictor variables should be on
## the same scale. Before centering and scaling the numeric predictors,
## any predictors with a single unique value are filtered out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_10_no_dummies_spec <-
multinom_reg(penalty = tune(), mixture = tune()) %>%
set_mode("classification") %>%
set_engine("glmnet")
test_config_10_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_10_no_dummies_recipe) %>%
add_model(test_config_10_no_dummies_spec)
test_config_10_no_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1,
length.out = 20), mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1))
test_config_10_no_dummies_tune <-
tune_grid(test_config_10_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = test_config_10_no_dummies_grid)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_11_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors. However, for this model, binary indicator variables can be
## made for each of the levels of the factors (known as 'one-hot
## encoding').
step_dummy(all_nominal_predictors(), one_hot = TRUE) %>%
step_zv(all_predictors())
test_config_11_dummies_spec <-
boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
loss_reduction = tune(), sample_size = tune()) %>%
set_mode("regression") %>%
set_engine("xgboost")
test_config_11_dummies_workflow <-
workflow() %>%
add_recipe(test_config_11_dummies_recipe) %>%
add_model(test_config_11_dummies_spec)
set.seed(27246)
test_config_11_dummies_tune <-
tune_grid(test_config_11_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_11_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors. However, for this model, binary indicator variables can be
## made for each of the levels of the factors (known as 'one-hot
## encoding').
step_dummy(all_nominal_predictors(), one_hot = TRUE) %>%
step_zv(all_predictors())
test_config_11_no_dummies_spec <-
boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
loss_reduction = tune(), sample_size = tune()) %>%
set_mode("classification") %>%
set_engine("xgboost")
test_config_11_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_11_no_dummies_recipe) %>%
add_model(test_config_11_no_dummies_spec)
set.seed(27246)
test_config_11_no_dummies_tune <-
tune_grid(test_config_11_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_12_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island"))
test_config_12_dummies_spec <-
rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>%
set_mode("regression") %>%
set_engine("ranger")
test_config_12_dummies_workflow <-
workflow() %>%
add_recipe(test_config_12_dummies_recipe) %>%
add_model(test_config_12_dummies_spec)
set.seed(27246)
test_config_12_dummies_tune <-
tune_grid(test_config_12_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_12_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island"))
test_config_12_no_dummies_spec <-
rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>%
set_mode("classification") %>%
set_engine("ranger")
test_config_12_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_12_no_dummies_recipe) %>%
add_model(test_config_12_no_dummies_spec)
set.seed(27246)
test_config_12_no_dummies_tune <-
tune_grid(test_config_12_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_13_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
## Since distance calculations are used, the predictor variables should
## be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_13_dummies_spec <-
nearest_neighbor(neighbors = tune(), weight_func = tune()) %>%
set_mode("regression") %>%
set_engine("kknn")
test_config_13_dummies_workflow <-
workflow() %>%
add_recipe(test_config_13_dummies_recipe) %>%
add_model(test_config_13_dummies_spec)
set.seed(27246)
test_config_13_dummies_tune <-
tune_grid(test_config_13_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_13_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
## Since distance calculations are used, the predictor variables should
## be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_13_no_dummies_spec <-
nearest_neighbor(neighbors = tune(), weight_func = tune()) %>%
set_mode("classification") %>%
set_engine("kknn")
test_config_13_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_13_no_dummies_recipe) %>%
add_model(test_config_13_no_dummies_spec)
set.seed(27246)
test_config_13_no_dummies_tune <-
tune_grid(test_config_13_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_14_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors())
test_config_14_dummies_spec <-
mars(num_terms = tune(), prod_degree = tune(), prune_method = "none") %>%
set_mode("regression") %>%
set_engine("earth")
test_config_14_dummies_workflow <-
workflow() %>%
add_recipe(test_config_14_dummies_recipe) %>%
add_model(test_config_14_dummies_spec)
## 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.
test_config_14_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2)
test_config_14_dummies_tune <-
tune_grid(test_config_14_dummies_workflow, resamples = stop("add your rsample object"),
grid = test_config_14_dummies_grid)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_14_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors())
test_config_14_no_dummies_spec <-
mars(num_terms = tune(), prod_degree = tune(), prune_method = "none") %>%
set_mode("classification") %>%
set_engine("earth")
test_config_14_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_14_no_dummies_recipe) %>%
add_model(test_config_14_no_dummies_spec)
## 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.
test_config_14_no_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2)
test_config_14_no_dummies_tune <-
tune_grid(test_config_14_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = test_config_14_no_dummies_grid)
Code
dummy_template(model, prefix, verbose, tune)
Output
library(rules)
test_config_15_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island")) %>%
step_zv(all_predictors())
test_config_15_dummies_spec <-
cubist_rules(committees = tune(), neighbors = tune()) %>%
set_engine("Cubist")
test_config_15_dummies_workflow <-
workflow() %>%
add_recipe(test_config_15_dummies_recipe) %>%
add_model(test_config_15_dummies_spec)
test_config_15_dummies_grid <- tidyr::crossing(committees = c(1:9, (1:5) *
10), neighbors = c(0, 3, 6, 9))
test_config_15_dummies_tune <-
tune_grid(test_config_15_dummies_workflow, resamples = stop("add your rsample object"),
grid = test_config_15_dummies_grid)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_16_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## Since dot product calculations are used, the predictor variables
## should be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_16_dummies_spec <-
svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>%
set_mode("regression")
test_config_16_dummies_workflow <-
workflow() %>%
add_recipe(test_config_16_dummies_recipe) %>%
add_model(test_config_16_dummies_spec)
set.seed(27246)
test_config_16_dummies_tune <-
tune_grid(test_config_16_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_16_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## Since dot product calculations are used, the predictor variables
## should be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_16_no_dummies_spec <-
svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>%
set_mode("classification")
test_config_16_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_16_no_dummies_recipe) %>%
add_model(test_config_16_no_dummies_spec)
set.seed(27246)
test_config_16_no_dummies_tune <-
tune_grid(test_config_16_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_17_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
## Since dot product calculations are used, the predictor variables
## should be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_17_dummies_spec <-
svm_rbf(cost = tune(), rbf_sigma = tune()) %>%
set_mode("regression")
test_config_17_dummies_workflow <-
workflow() %>%
add_recipe(test_config_17_dummies_recipe) %>%
add_model(test_config_17_dummies_spec)
set.seed(27246)
test_config_17_dummies_tune <-
tune_grid(test_config_17_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_17_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## Since dot product calculations are used, the predictor variables
## should be on the same scale. Before centering and scaling the numeric
## predictors, any predictors with a single unique value are filtered
## out.
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_17_no_dummies_spec <-
svm_rbf(cost = tune(), rbf_sigma = tune()) %>%
set_mode("classification")
test_config_17_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_17_no_dummies_recipe) %>%
add_model(test_config_17_no_dummies_spec)
set.seed(27246)
test_config_17_no_dummies_tune <-
tune_grid(test_config_17_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_18_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
step_string2factor(one_of("island"))
test_config_18_no_dummies_spec <-
boost_tree(trees = tune(), min_n = tune()) %>%
set_mode("classification") %>%
set_engine("C5.0")
test_config_18_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_18_no_dummies_recipe) %>%
add_model(test_config_18_no_dummies_spec)
set.seed(27246)
test_config_18_no_dummies_tune <-
tune_grid(test_config_18_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_19_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_19_dummies_spec <-
linear_reg() %>%
set_mode("regression") %>%
set_engine("glmnet")
test_config_19_dummies_workflow <-
workflow() %>%
add_recipe(test_config_19_dummies_recipe) %>%
add_model(test_config_19_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_19_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_19_no_dummies_spec <-
multinom_reg() %>%
set_mode("classification") %>%
set_engine("glmnet")
test_config_19_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_19_no_dummies_recipe) %>%
add_model(test_config_19_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_20_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors(), one_hot = TRUE) %>%
step_zv(all_predictors())
test_config_20_dummies_spec <-
boost_tree() %>%
set_mode("regression") %>%
set_engine("xgboost")
test_config_20_dummies_workflow <-
workflow() %>%
add_recipe(test_config_20_dummies_recipe) %>%
add_model(test_config_20_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_20_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors(), one_hot = TRUE) %>%
step_zv(all_predictors())
test_config_20_no_dummies_spec <-
boost_tree() %>%
set_mode("classification") %>%
set_engine("xgboost")
test_config_20_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_20_no_dummies_recipe) %>%
add_model(test_config_20_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_21_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island"))
test_config_21_dummies_spec <-
rand_forest(trees = 1000) %>%
set_mode("regression") %>%
set_engine("ranger")
test_config_21_dummies_workflow <-
workflow() %>%
add_recipe(test_config_21_dummies_recipe) %>%
add_model(test_config_21_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_21_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island"))
test_config_21_no_dummies_spec <-
rand_forest(trees = 1000) %>%
set_mode("classification") %>%
set_engine("ranger")
test_config_21_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_21_no_dummies_recipe) %>%
add_model(test_config_21_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_22_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_22_dummies_spec <-
nearest_neighbor() %>%
set_mode("regression") %>%
set_engine("kknn")
test_config_22_dummies_workflow <-
workflow() %>%
add_recipe(test_config_22_dummies_recipe) %>%
add_model(test_config_22_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_22_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_22_no_dummies_spec <-
nearest_neighbor() %>%
set_mode("classification") %>%
set_engine("kknn")
test_config_22_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_22_no_dummies_recipe) %>%
add_model(test_config_22_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_23_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors())
test_config_23_dummies_spec <-
mars() %>%
set_mode("regression") %>%
set_engine("earth")
test_config_23_dummies_workflow <-
workflow() %>%
add_recipe(test_config_23_dummies_recipe) %>%
add_model(test_config_23_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_23_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors())
test_config_23_no_dummies_spec <-
mars() %>%
set_mode("classification") %>%
set_engine("earth")
test_config_23_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_23_no_dummies_recipe) %>%
add_model(test_config_23_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
library(rules)
test_config_24_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_zv(all_predictors())
test_config_24_dummies_spec <-
cubist_rules() %>%
set_engine("Cubist")
test_config_24_dummies_workflow <-
workflow() %>%
add_recipe(test_config_24_dummies_recipe) %>%
add_model(test_config_24_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_25_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_25_dummies_spec <-
svm_poly() %>%
set_mode("regression")
test_config_25_dummies_workflow <-
workflow() %>%
add_recipe(test_config_25_dummies_recipe) %>%
add_model(test_config_25_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_25_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_25_no_dummies_spec <-
svm_poly() %>%
set_mode("classification")
test_config_25_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_25_no_dummies_recipe) %>%
add_model(test_config_25_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_26_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_26_dummies_spec <-
svm_rbf() %>%
set_mode("regression")
test_config_26_dummies_workflow <-
workflow() %>%
add_recipe(test_config_26_dummies_recipe) %>%
add_model(test_config_26_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_26_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_26_no_dummies_spec <-
svm_rbf() %>%
set_mode("classification")
test_config_26_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_26_no_dummies_recipe) %>%
add_model(test_config_26_no_dummies_spec)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_27_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island"))
test_config_27_no_dummies_spec <-
boost_tree() %>%
set_mode("classification") %>%
set_engine("C5.0")
test_config_27_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_27_no_dummies_recipe) %>%
add_model(test_config_27_no_dummies_spec)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_28_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_28_dummies_spec <-
linear_reg(penalty = tune(), mixture = tune()) %>%
set_mode("regression") %>%
set_engine("glmnet")
test_config_28_dummies_workflow <-
workflow() %>%
add_recipe(test_config_28_dummies_recipe) %>%
add_model(test_config_28_dummies_spec)
test_config_28_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1, length.out = 20),
mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1))
test_config_28_dummies_tune <-
tune_grid(test_config_28_dummies_workflow, resamples = stop("add your rsample object"),
grid = test_config_28_dummies_grid)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_28_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_28_no_dummies_spec <-
multinom_reg(penalty = tune(), mixture = tune()) %>%
set_mode("classification") %>%
set_engine("glmnet")
test_config_28_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_28_no_dummies_recipe) %>%
add_model(test_config_28_no_dummies_spec)
test_config_28_no_dummies_grid <- tidyr::crossing(penalty = 10^seq(-6, -1,
length.out = 20), mixture = c(0.05, 0.2, 0.4, 0.6, 0.8, 1))
test_config_28_no_dummies_tune <-
tune_grid(test_config_28_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = test_config_28_no_dummies_grid)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_29_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors(), one_hot = TRUE) %>%
step_zv(all_predictors())
test_config_29_dummies_spec <-
boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
loss_reduction = tune(), sample_size = tune()) %>%
set_mode("regression") %>%
set_engine("xgboost")
test_config_29_dummies_workflow <-
workflow() %>%
add_recipe(test_config_29_dummies_recipe) %>%
add_model(test_config_29_dummies_spec)
set.seed(27246)
test_config_29_dummies_tune <-
tune_grid(test_config_29_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_29_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors(), one_hot = TRUE) %>%
step_zv(all_predictors())
test_config_29_no_dummies_spec <-
boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
loss_reduction = tune(), sample_size = tune()) %>%
set_mode("classification") %>%
set_engine("xgboost")
test_config_29_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_29_no_dummies_recipe) %>%
add_model(test_config_29_no_dummies_spec)
set.seed(27246)
test_config_29_no_dummies_tune <-
tune_grid(test_config_29_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_30_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island"))
test_config_30_dummies_spec <-
rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>%
set_mode("regression") %>%
set_engine("ranger")
test_config_30_dummies_workflow <-
workflow() %>%
add_recipe(test_config_30_dummies_recipe) %>%
add_model(test_config_30_dummies_spec)
set.seed(27246)
test_config_30_dummies_tune <-
tune_grid(test_config_30_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_30_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island"))
test_config_30_no_dummies_spec <-
rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>%
set_mode("classification") %>%
set_engine("ranger")
test_config_30_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_30_no_dummies_recipe) %>%
add_model(test_config_30_no_dummies_spec)
set.seed(27246)
test_config_30_no_dummies_tune <-
tune_grid(test_config_30_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_31_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_31_dummies_spec <-
nearest_neighbor(neighbors = tune(), weight_func = tune()) %>%
set_mode("regression") %>%
set_engine("kknn")
test_config_31_dummies_workflow <-
workflow() %>%
add_recipe(test_config_31_dummies_recipe) %>%
add_model(test_config_31_dummies_spec)
set.seed(27246)
test_config_31_dummies_tune <-
tune_grid(test_config_31_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_31_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_31_no_dummies_spec <-
nearest_neighbor(neighbors = tune(), weight_func = tune()) %>%
set_mode("classification") %>%
set_engine("kknn")
test_config_31_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_31_no_dummies_recipe) %>%
add_model(test_config_31_no_dummies_spec)
set.seed(27246)
test_config_31_no_dummies_tune <-
tune_grid(test_config_31_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_32_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors())
test_config_32_dummies_spec <-
mars(num_terms = tune(), prod_degree = tune(), prune_method = "none") %>%
set_mode("regression") %>%
set_engine("earth")
test_config_32_dummies_workflow <-
workflow() %>%
add_recipe(test_config_32_dummies_recipe) %>%
add_model(test_config_32_dummies_spec)
test_config_32_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2)
test_config_32_dummies_tune <-
tune_grid(test_config_32_dummies_workflow, resamples = stop("add your rsample object"),
grid = test_config_32_dummies_grid)
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_32_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_novel(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors())
test_config_32_no_dummies_spec <-
mars(num_terms = tune(), prod_degree = tune(), prune_method = "none") %>%
set_mode("classification") %>%
set_engine("earth")
test_config_32_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_32_no_dummies_recipe) %>%
add_model(test_config_32_no_dummies_spec)
test_config_32_no_dummies_grid <- tidyr::crossing(num_terms = 2 * (1:6), prod_degree = 1:2)
test_config_32_no_dummies_tune <-
tune_grid(test_config_32_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = test_config_32_no_dummies_grid)
Code
dummy_template(model, prefix, verbose, tune)
Output
library(rules)
test_config_33_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_string2factor(one_of("island")) %>%
step_zv(all_predictors())
test_config_33_dummies_spec <-
cubist_rules(committees = tune(), neighbors = tune()) %>%
set_engine("Cubist")
test_config_33_dummies_workflow <-
workflow() %>%
add_recipe(test_config_33_dummies_recipe) %>%
add_model(test_config_33_dummies_spec)
test_config_33_dummies_grid <- tidyr::crossing(committees = c(1:9, (1:5) *
10), neighbors = c(0, 3, 6, 9))
test_config_33_dummies_tune <-
tune_grid(test_config_33_dummies_workflow, resamples = stop("add your rsample object"),
grid = test_config_33_dummies_grid)
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_34_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_34_dummies_spec <-
svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>%
set_mode("regression")
test_config_34_dummies_workflow <-
workflow() %>%
add_recipe(test_config_34_dummies_recipe) %>%
add_model(test_config_34_dummies_spec)
set.seed(27246)
test_config_34_dummies_tune <-
tune_grid(test_config_34_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_34_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_34_no_dummies_spec <-
svm_poly(cost = tune(), degree = tune(), scale_factor = tune()) %>%
set_mode("classification")
test_config_34_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_34_no_dummies_recipe) %>%
add_model(test_config_34_no_dummies_spec)
set.seed(27246)
test_config_34_no_dummies_tune <-
tune_grid(test_config_34_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
dummy_template(model, prefix, verbose, tune)
Output
test_config_35_dummies_recipe <-
recipe(formula = body_mass_g ~ ., data = penguins) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_35_dummies_spec <-
svm_rbf(cost = tune(), rbf_sigma = tune()) %>%
set_mode("regression")
test_config_35_dummies_workflow <-
workflow() %>%
add_recipe(test_config_35_dummies_recipe) %>%
add_model(test_config_35_dummies_spec)
set.seed(27246)
test_config_35_dummies_tune <-
tune_grid(test_config_35_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_35_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_zv(all_predictors()) %>%
step_normalize(all_numeric_predictors())
test_config_35_no_dummies_spec <-
svm_rbf(cost = tune(), rbf_sigma = tune()) %>%
set_mode("classification")
test_config_35_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_35_no_dummies_recipe) %>%
add_model(test_config_35_no_dummies_spec)
set.seed(27246)
test_config_35_no_dummies_tune <-
tune_grid(test_config_35_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
Code
no_dummy_template(model, prefix, verbose, tune)
Output
test_config_36_no_dummies_recipe <-
recipe(formula = species ~ ., data = penguins) %>%
step_string2factor(one_of("island"))
test_config_36_no_dummies_spec <-
boost_tree(trees = tune(), min_n = tune()) %>%
set_mode("classification") %>%
set_engine("C5.0")
test_config_36_no_dummies_workflow <-
workflow() %>%
add_recipe(test_config_36_no_dummies_recipe) %>%
add_model(test_config_36_no_dummies_spec)
set.seed(27246)
test_config_36_no_dummies_tune <-
tune_grid(test_config_36_no_dummies_workflow, resamples = stop("add your rsample object"),
grid = stop("add number of candidate points"))
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