regression.train_model: Train a tidymodels model via workflows

In shapr: Prediction Explanation with Dependence-Aware Shapley Values

Train a tidymodels model via workflows

Description

Function that trains a tidymodels model via workflows based on the provided input parameters. This function allows for cross validating the hyperparameters of the model.

Usage

regression.train_model(
  x,
  seed = 1,
  verbose = NULL,
  regression.model = parsnip::linear_reg(),
  regression.tune = FALSE,
  regression.tune_values = NULL,
  regression.vfold_cv_para = NULL,
  regression.recipe_func = NULL,
  regression.response_var = "y_hat",
  regression.surrogate_n_comb = NULL,
  current_comb = NULL
)

Arguments

x	Data.table containing the training data.
seed	Positive integer. Specifies the seed before any code involving randomness is run. If NULL (default), no seed is set in the calling environment.
verbose	String vector or NULL. Controls verbosity (printout detail level) via one or more of "basic", "progress", "convergence", "shapley" and "vS_details". "basic" (default) displays basic information about the computation and messages about parameters/checks. "progress" displays where in the calculation process the function currently is. "convergence" displays how close the Shapley value estimates are to convergence (only when iterative = TRUE). "shapley" displays intermediate Shapley value estimates and standard deviations (only when iterative = TRUE), and the final estimates. "vS_details" displays information about the v(S) estimates, most relevant for approach %in% c("regression_separate", "regression_surrogate", "vaeac"). NULL means no printout. Any combination can be used, e.g., verbose = c("basic", "vS_details").
regression.model	A tidymodels object of class model_specs. Default is a linear regression model, i.e., parsnip::linear_reg(). See tidymodels for all possible models, and see the vignette for how to add new/own models. Note, to make it easier to call explain() from Python, the regression.model parameter can also be a string specifying the model which will be parsed and evaluated. For example, ⁠"parsnip::rand_forest(mtry = hardhat::tune(), trees = 100, engine = "ranger", mode = "regression")"⁠ is also a valid input. It is essential to include the package prefix if the package is not loaded.
regression.tune	Logical (default is FALSE). If TRUE, then we are to tune the hyperparemeters based on the values provided in regression.tune_values. Note that no checks are conducted as this is checked earlier in setup_approach.regression_separate and setup_approach.regression_surrogate.
regression.tune_values	Either NULL (default), a data.frame/data.table/tibble, or a function. The data.frame must contain the possible hyperparameter value combinations to try. The column names must match the names of the tunable parameters specified in regression.model. If regression.tune_values is a function, then it should take one argument x which is the training data for the current coalition and returns a data.frame/data.table/tibble with the properties described above. Using a function allows the hyperparameter values to change based on the size of the coalition See the regression vignette for several examples. Note, to make it easier to call explain() from Python, the regression.tune_values can also be a string containing an R function. For example, "function(x) return(dials::grid_regular(dials::mtry(c(1, ncol(x)))), levels = 3))" is also a valid input. It is essential to include the package prefix if the package is not loaded.
regression.vfold_cv_para	Either NULL (default) or a named list containing the parameters to be sent to rsample::vfold_cv(). See the regression vignette for several examples.
regression.recipe_func	Either NULL (default) or a function that that takes in a recipes::recipe() object and returns a modified recipes::recipe() with potentially additional recipe steps. See the regression vignette for several examples. Note, to make it easier to call explain() from Python, the regression.recipe_func can also be a string containing an R function. For example, "function(recipe) return(recipes::step_ns(recipe, recipes::all_numeric_predictors(), deg_free = 2))" is also a valid input. It is essential to include the package prefix if the package is not loaded.
regression.response_var	String (default is y_hat) containing the name of the response variable.
regression.surrogate_n_comb	Integer (default is NULL). The number of times each training observations has been augmented. If NULL, then we assume that we are doing separate regression.
current_comb	Integer vector. The current combination of features, passed to verbosity printing function.

Value

A trained tidymodels model based on the provided input parameters.

Author(s)

Lars Henry Berge Olsen

shapr documentation built on Aug. 25, 2025, 5:11 p.m.