Train a linear model using LASSO regression.

Train a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) regression.

cuda_ml_lasso(x, ...)

# S3 method for default
cuda_ml_lasso(x, ...)

# S3 method for data.frame
cuda_ml_lasso(
  x,
  y,
  alpha = 1,
  max_iter = 1000L,
  tol = 0.001,
  fit_intercept = TRUE,
  normalize_input = FALSE,
  selection = c("cyclic", "random"),
  ...
)

# S3 method for matrix
cuda_ml_lasso(
  x,
  y,
  alpha = 1,
  max_iter = 1000L,
  tol = 0.001,
  fit_intercept = TRUE,
  normalize_input = FALSE,
  selection = c("cyclic", "random"),
  ...
)

# S3 method for formula
cuda_ml_lasso(
  formula,
  data,
  alpha = 1,
  max_iter = 1000L,
  tol = 0.001,
  fit_intercept = TRUE,
  normalize_input = FALSE,
  selection = c("cyclic", "random"),
  ...
)

# S3 method for recipe
cuda_ml_lasso(
  x,
  data,
  alpha = 1,
  max_iter = 1000L,
  tol = 0.001,
  fit_intercept = TRUE,
  normalize_input = FALSE,
  selection = c("cyclic", "random"),
  ...
)

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	Multiplier of the L1 penalty term (i.e., the result would become and Ordinary Least Square model if alpha were set to 0). Default: 1.
max_iter	The maximum number of coordinate descent iterations. Default: 1000L.
tol	Stop the coordinate descent when the duality gap is below this threshold. Default: 1e-3.
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.
normalize_input	Ignored when fit_intercept is FALSE. If TRUE, then the predictors will be normalized to have a L2 norm of 1. Default: FALSE.
selection	If "random", then instead of updating coefficients in cyclic order, a random coefficient is updated in each iteration. Default: "cyclic".
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 LASSO regressor that can be used with the 'predict' S3 generic to make predictions on new data points.

Examples

library(cuda.ml)

model <- cuda_ml_lasso(formula = mpg ~ ., data = mtcars, alpha = 1e-3)
predictions <- predict(model, mtcars)

# predictions will be comparable to those from a `glmnet` model with `lambda`
# set to 1e-3 and `alpha` set to 1
# (in `glmnet`, `lambda` is the weight of the penalty term, and `alpha` is
#  the elastic mixing parameter between L1 and L2 penalties.

library(glmnet)
#> Loading required package: Matrix
#> Loaded glmnet 4.1-2

glmnet_model <- glmnet(
  x = as.matrix(mtcars[names(mtcars) != "mpg"]), y = mtcars$mpg,
  alpha = 1, lambda = 1e-3, nlambda = 1, standardize = FALSE
)

glm_predictions <- predict(
  glmnet_model, as.matrix(mtcars[names(mtcars) != "mpg"]), s = 0
)

print(max(abs(glm_predictions - predictions$.pred)))
#> [1] 29.87654