This learner provides fitting procedures for a fast implementation of Random Forests, particularly suited for high dimensional data, using the ranger package, using the function ranger.

Lrnr_ranger

Format

R6Class object.

Value

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

Parameters

num.trees = 500

Number of trees to be used in growing the forest.

write.forest = TRUE

If TRUE, forest is stored, which is required for prediction. Set to FALSE to reduce memory usage if no prediction is intended.

...

Other parameters passed to ranger. See its documentation for details.

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_glmnet, 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_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