This learner provides fitting procedures for random forest models, using the randomForest package, using randomForest function.

Lrnr_randomForest

Format

R6Class object.

Value

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

Parameters

ntree=100

Number of trees in forest

keep.forest=TRUE

If TRUE, forest is stored, which is required for prediction.

nodesize=5

Minimum number of observations in terminal (leaf) nodes.

maxnodes=NULL

Maximum number of terminal (leaf) nodes in each tree.

importance=FALSE

Store variable importance information.

...

Other parameters passed to randomForest.

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_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