This learner provides fitting procedures for random forest models, using the
randomForest
package, using randomForest
function.
Lrnr_randomForest
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
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
.
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