Train a logistic or multinomial regression model

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().