Train a regularized linear regression model — cuda_ml_linear

This is the tidymodels-style linear regression interface. It dispatches to cuda_ml_ols(), cuda_ml_ridge(), cuda_ml_lasso(), or cuda_ml_elastic_net() according to penalty and mixture. The named model functions remain available when direct control over their solver arguments is needed.

Usage

cuda_ml_linear_reg(formula, data, penalty = NULL, mixture = NULL, ...)

Arguments

formula: A model formula.
data: A data frame containing predictors and outcome.
penalty: A non-negative regularization strength, or NULL for no regularization.
mixture: The proportion of regularization assigned to the L1 penalty, between 0 and 1. When NULL, a lasso penalty is used.
...: Arguments passed to the selected named model function.

Value

A fitted cuda.ml linear model.