Train a linear model using ridge regression.

Train a linear model with L2 regularization.

Usage

cuda_ml_ridge(x, ...)

# Default S3 method
cuda_ml_ridge(x, ...)

# S3 method for class 'data.frame'
cuda_ml_ridge(x, y, alpha = 1, fit_intercept = TRUE, ...)

# S3 method for class 'matrix'
cuda_ml_ridge(x, y, alpha = 1, fit_intercept = TRUE, ...)

# S3 method for class 'formula'
cuda_ml_ridge(formula, data, alpha = 1, fit_intercept = TRUE, ...)

# S3 method for class 'recipe'
cuda_ml_ridge(x, data, alpha = 1, fit_intercept = 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.

alpha

Positive multiplier of the L2 penalty term. Use cuda_ml_ols() for an unpenalized linear model. Default: 1.

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.

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 ridge regressor that can be used with the 'predict' S3 generic to make predictions on new data points.

Examples

library(cuda.ml)

if (interactive() && cuda_ml_backend_info()$runtime_installed) {
  model <- cuda_ml_ridge(formula = mpg ~ ., data = mtcars, alpha = 1e-3)
  predictors <- subset(mtcars, select = -mpg)
  cuda_ml_predictions <- predict(model, predictors)

  # predictions will be comparable to those from a `glmnet` model with
  # `lambda` set to 2e-3 and `alpha` set to 0
  # (in `glmnet`, `lambda` is the weight of the penalty term, and `alpha` is
  #  the elastic mixing parameter between L1 and L2 penalties.

  if (requireNamespace("glmnet", quietly = TRUE)) {
    glmnet_model <- glmnet::glmnet(
      x = as.matrix(predictors), y = mtcars$mpg,
      alpha = 0, lambda = 2e-3, nlambda = 1, standardize = FALSE
    )

    glmnet_predictions <- predict(
      glmnet_model, as.matrix(predictors),
      s = 0
    )

    print(
      all.equal(
        as.numeric(glmnet_predictions),
        cuda_ml_predictions$.pred,
        tolerance = 1e-3
      )
    )
  }
}