Lrnr_screener_coefs: Coefficient Magnitude Screener

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

Coefficient Magnitude Screener

Description

This learner provides screening of covariates based on the magnitude of their estimated coefficients in a (possibly regularized) GLM.

Format

R6Class object.

Value

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

Parameters

learner

An instantiated learner to use for estimating coefficients used in screening.

threshold = 1e-3

Minimum size of coefficients to be kept.

max_screen = NULL

Maximum number of covariates to be kept.

min_screen = 2

Maximum number of covariates to be kept. Only applicable when supplied learner is a Lrnr_glmnet.

...

Other parameters passed to learner.

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_glmnet, 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_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

library(data.table)

# load example data
data(cpp_imputed)
setDT(cpp_imputed)
cpp_imputed[, parity_cat := factor(ifelse(parity < 4, parity, 4))]
covars <- c(
  "apgar1", "apgar5", "parity_cat", "gagebrth", "mage", "meducyrs",
  "sexn"
)
outcome <- "haz"

# create sl3 task
task <- sl3_Task$new(data.table::copy(cpp_imputed),
  covariates = covars,
  outcome = outcome
)

lrnr_glmnet <- make_learner(Lrnr_glmnet)
lrnr_glm <- make_learner(Lrnr_glm)
lrnr_mean <- make_learner(Lrnr_mean)
lrnrs <- make_learner(Stack, lrnr_glm, lrnr_mean)

glm_screener <- make_learner(Lrnr_screener_coefs, lrnr_glm, max_screen = 2)
glm_screener_pipeline <- make_learner(Pipeline, glm_screener, lrnrs)
fit_glm_screener_pipeline <- glm_screener_pipeline$train(task)
preds_glm_screener_pipeline <- fit_glm_screener_pipeline$predict()

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