This learner provides fitting procedures for elastic net models, using the glmnet package, using cv.glmnet to select an appropriate value of lambda.

Lrnr_glmnet

Format

R6Class object.

Value

Learner object with methods for training and prediction. See Lrnr_base for documentation on learners.

Parameters

lambda=NULL

A vector of lambda values to compare

type.measure="deviance"

The loss to use when selecting lambda. Options documented in cv.glmnet.

nfolds=10

Number of folds to use for internal cross-validation.

alpha=1

The elastic net parameter. 0 is Ridge Regression, 1 is Lasso. Intermediate values are a combination. Documented in glmnet.

nlambda=100

The number of lambda values to compare. Comparing less values will speed up computation, but may decrease statistical performance. Documented in cv.glmnet.

use_min=TRUE

If TRUE, use lambda=cv_fit$lambda.min for prediction, otherwise use lambda=cv_fit$lambda.1se. the distinction is clarified in cv.glmnet.

...

Other parameters to be passed to cv.glmnet and glmnet.

Common Parameters

Individual learners have their own sets of parameters. Below is a list of shared parameters, implemented by Lrnr_base, and shared by all learners.

covariates

A character vector of covariates. The learner will use this to subset the covariates for any specified task

outcome_type

A variable_type object used to control the outcome_type used by the learner. Overrides the task outcome_type if specified

...

All other parameters should be handled by the invidual learner classes. See the documentation for the learner class you're instantiating

See also

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bilstm, Lrnr_condensier, Lrnr_cv, Lrnr_define_interactions, Lrnr_expSmooth, Lrnr_glm_fast, Lrnr_glm, Lrnr_h2o_grid, Lrnr_hal9001, Lrnr_independent_binomial, Lrnr_lstm, Lrnr_mean, Lrnr_nnls, Lrnr_optim, Lrnr_pca, Lrnr_pkg_SuperLearner, Lrnr_randomForest, Lrnr_ranger, Lrnr_rpart, Lrnr_rugarch, Lrnr_sl, Lrnr_solnp_density, Lrnr_solnp, Lrnr_subset_covariates, Lrnr_svm, Lrnr_tsDyn, Lrnr_xgboost, Pipeline, Stack, define_h2o_X, undocumented_learner