Lrnr_glm: Generalized Linear Models

Description Usage Format Value Parameters Common Parameters See Also


This learner provides fitting procedures for generalized linear models using




R6Class object.


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



Parameters passed to glm.

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.


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


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_dbarts, Lrnr_define_interactions, Lrnr_expSmooth, Lrnr_glm_fast, Lrnr_glmnet, Lrnr_grf, 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

jeremyrcoyle/sl3 documentation built on Oct. 13, 2018, 8:55 p.m.