regression.get_tune: Get if model is to be tuned

In NorskRegnesentral/shapr: Prediction Explanation with Dependence-Aware Shapley Values

Get if model is to be tuned

Description

That is, if the regression model contains hyperparameters we are to tune using cross validation. See tidymodels for default model hyperparameters.

Usage

regression.get_tune(regression.model, regression.tune_values, x_train)

Arguments

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_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 tuneable 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 combination/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 combination. 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.
x_train	Matrix or data.frame/data.table. Contains the data used to estimate the (conditional) distributions for the features needed to properly estimate the conditional expectations in the Shapley formula.

Value

A boolean variable indicating if the regression model is to be tuned.

Author(s)

Lars Henry Berge Olsen

NorskRegnesentral/shapr documentation built on April 19, 2024, 1:19 p.m.