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
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
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.
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