cuda_ml_logistic_reg: Train a logistic or multinomial regression model

In cuda.ml: R Interface for the RAPIDS cuML Suite of Libraries

Train a logistic or multinomial regression model

Description

Fits a factor outcome with cuML's quasi-Newton solver. Regularization follows tidymodels conventions: penalty is the total regularization strength and mixture is the proportion assigned to the L1 penalty. Set mixture = 0 for ridge, mixture = 1 for lasso, or use an intermediate value for an elastic-net penalty. Normalize predictors with a recipe before fitting when scaling is required.

Usage

cuda_ml_logistic_reg(x, ...)

## Default S3 method:
cuda_ml_logistic_reg(x, ...)

## S3 method for class 'data.frame'
cuda_ml_logistic_reg(
  x,
  y,
  fit_intercept = TRUE,
  penalty = NULL,
  mixture = 0,
  tol = 1e-04,
  class_weight = NULL,
  sample_weight = NULL,
  max_iter = 1000L,
  linesearch_max_iter = 50L,
  lbfgs_memory = 5L,
  penalty_normalized = TRUE,
  ...
)

## S3 method for class 'matrix'
cuda_ml_logistic_reg(
  x,
  y,
  fit_intercept = TRUE,
  penalty = NULL,
  mixture = 0,
  tol = 1e-04,
  class_weight = NULL,
  sample_weight = NULL,
  max_iter = 1000L,
  linesearch_max_iter = 50L,
  lbfgs_memory = 5L,
  penalty_normalized = TRUE,
  ...
)

## S3 method for class 'formula'
cuda_ml_logistic_reg(
  formula,
  data,
  fit_intercept = TRUE,
  penalty = NULL,
  mixture = 0,
  tol = 1e-04,
  class_weight = NULL,
  sample_weight = NULL,
  max_iter = 1000L,
  linesearch_max_iter = 50L,
  lbfgs_memory = 5L,
  penalty_normalized = TRUE,
  ...
)

## S3 method for class 'recipe'
cuda_ml_logistic_reg(
  x,
  data,
  fit_intercept = TRUE,
  penalty = NULL,
  mixture = 0,
  tol = 1e-04,
  class_weight = NULL,
  sample_weight = NULL,
  max_iter = 1000L,
  linesearch_max_iter = 50L,
  lbfgs_memory = 5L,
  penalty_normalized = TRUE,
  ...
)

Arguments

x	Depending on the context: A data frame of predictors. A matrix of predictors. A recipe specifying a set of preprocessing steps created from recipes::recipe(). A formula specifying the predictors and the outcome.
...	Optional arguments; currently unused.
y	A numeric vector (for regression) or factor (for classification) of desired responses.
fit_intercept	If TRUE, then the model tries to correct for the global mean of the response variable. If FALSE, then the model expects data to be centered. Default: TRUE.
penalty	A non-negative regularization strength, or NULL for no regularization. Default: NULL.
mixture	The proportion of regularization assigned to the L1 penalty, between 0 and 1. Default: 0.
tol	Stopping tolerance. Default: 1e-4.
class_weight	NULL, "balanced", or a named numeric vector with one non-negative weight per outcome level.
sample_weight	A numeric vector with one non-negative weight per training observation, or NULL.
max_iter	Maximum solver iterations. Default: 1000L.
linesearch_max_iter	Maximum line-search iterations per solver iteration. Default: 50L.
lbfgs_memory	Number of vectors retained by the L-BFGS approximation. Default: 5L.
penalty_normalized	Whether to normalize regularization by the number of observations. Default: TRUE.
formula	A formula specifying the outcome terms on the left-hand side, and the predictor terms on the right-hand side.
data	When a recipe or formula is used, data is specified as a data frame containing the predictors and (if applicable) the outcome.

Value

A classification model for use with predict().

cuda.ml documentation built on Aug. 21, 2026, 9:14 a.m.