Lrnr_glmnet: GLMs with Elastic Net Regularization

In tlverse/sl3: Pipelines for Machine Learning and Super Learning

GLMs with Elastic Net Regularization

Description

This learner provides fitting procedures for elastic net models, including both lasso (L1) and ridge (L2) penalized regression, using the glmnet package. The function cv.glmnet is used to select an appropriate value of the regularization parameter lambda. For details on these regularized regression models and glmnet, consider consulting \insertCiteglmnet;textualsl3).

Format

An R6Class object inheriting from Lrnr_base.

Value

A learner object inheriting from Lrnr_base with methods for training and prediction. For a full list of learner functionality, see the complete documentation of Lrnr_base.

Parameters

• lambda = NULL: An optional 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 k-fold/V-fold cross-validation folds for cv.glmnet to consider when selecting the optimal lambda with cross-validation. Smallest nfolds value allowed by glmnet is 3. For further details, consult the documentation of cv.glmnet.

• alpha = 1: The elastic net parameter: alpha = 0 is Ridge (L2-penalized) regression, while alpha = 1 specifies Lasso (L1-penalized) regression. Values in the closed unit interval specify a weighted combination of the two penalties. For further details, consult the documentation of glmnet.

• nlambda = 100: The number of lambda values to fit. Comparing fewer values will speed up computation, but may hurt the statistical performance. For further details, consult the documentation of cv.glmnet.

• use_min = TRUE: If TRUE, the smallest value of the lambda regularization parameter is used for prediction (i.e., lambda = cv_fit$lambda.min); otherwise, a larger value is used (i.e., lambda = cv_fit$lambda.1se). The distinction between the two variants is clarified in the documentation of cv.glmnet.

• nfolds = 10: Number of folds (default is 10). Smallest value allowable by glmnet is 3.

• ...: Other parameters passed to cv.glmnet and glmnet, and additional arguments defined in Lrnr_base, such as params like formula.

References

\insertAllCited

See Also

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bayesglm, Lrnr_caret, Lrnr_cv_selector, Lrnr_cv, Lrnr_dbarts, Lrnr_define_interactions, Lrnr_density_discretize, Lrnr_density_hse, Lrnr_density_semiparametric, Lrnr_earth, Lrnr_expSmooth, Lrnr_gam, Lrnr_ga, Lrnr_gbm, Lrnr_glm_fast, Lrnr_glm_semiparametric, Lrnr_glmtree, Lrnr_glm, Lrnr_grfcate, Lrnr_grf, Lrnr_gru_keras, Lrnr_gts, Lrnr_h2o_grid, Lrnr_hal9001, Lrnr_haldensify, Lrnr_hts, Lrnr_independent_binomial, Lrnr_lightgbm, Lrnr_lstm_keras, Lrnr_mean, Lrnr_multiple_ts, Lrnr_multivariate, Lrnr_nnet, Lrnr_nnls, Lrnr_optim, Lrnr_pca, Lrnr_pkg_SuperLearner, Lrnr_polspline, Lrnr_pooled_hazards, Lrnr_randomForest, Lrnr_ranger, Lrnr_revere_task, Lrnr_rpart, Lrnr_rugarch, Lrnr_screener_augment, Lrnr_screener_coefs, Lrnr_screener_correlation, Lrnr_screener_importance, Lrnr_sl, Lrnr_solnp_density, Lrnr_solnp, Lrnr_stratified, Lrnr_subset_covariates, Lrnr_svm, Lrnr_tsDyn, Lrnr_ts_weights, Lrnr_xgboost, Pipeline, Stack, define_h2o_X(), undocumented_learner

Examples

data(mtcars)
mtcars_task <- sl3_Task$new(
  data = mtcars,
  covariates = c(
    "cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "am",
    "gear", "carb"
  ),
  outcome = "mpg"
)
# simple prediction with lasso penalty
lasso_lrnr <- Lrnr_glmnet$new()
lasso_fit <- lasso_lrnr$train(mtcars_task)
lasso_preds <- lasso_fit$predict()

# simple prediction with ridge penalty
ridge_lrnr <- Lrnr_glmnet$new(alpha = 0)
ridge_fit <- ridge_lrnr$train(mtcars_task)
ridge_preds <- ridge_fit$predict()

tlverse/sl3 documentation built on Nov. 18, 2024, 12:46 a.m.