setup_GLMNET: Setup GLMNET Hyperparameters

In rtemis: Schema-Driven Machine Learning and Visualization

Setup GLMNET Hyperparameters

Description

Setup hyperparameters for GLMNET training.

Usage

setup_GLMNET(
  alpha = 1,
  family = NULL,
  which_lambda_cv = "lambda.1se",
  nlambda = 100L,
  lambda = NULL,
  penalty_factor = NULL,
  standardize = TRUE,
  intercept = TRUE,
  ifw = TRUE
)

Arguments

alpha	(Tunable) Numeric [0, 1]: Elastic net mixing parameter.
family	Optional Character {"gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"}: Family. NULL = set from outcome type.
which_lambda_cv	Character {"lambda.1se", "lambda.min"}: Which lambda to use for prediction.
nlambda	Integer [1, Inf): Number of lambda values.
lambda	Optional Numeric [0, Inf) vector: Lambda values. NULL = determined by cv.glmnet during tuning.
penalty_factor	Optional Numeric [0, Inf) vector: Penalty factor for each design-matrix column (factors expanded to indicator columns). NULL uses 1 for every column.
standardize	Logical: If TRUE, standardize features.
intercept	Logical: If TRUE, include intercept.
ifw	(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in classification.

Details

Get more information from glmnet::glmnet.

Value

GLMNETHyperparameters object.

Author(s)

EDG

Examples

glm_hyperparams <- setup_GLMNET(alpha = 1, ifw = TRUE)
glm_hyperparams

rtemis documentation built on Oct. 20, 2026, 1:06 a.m.