Description Format Value Parameters See Also
This learner provides fitting procedures for random forest models, using the
randomForest
package, using randomForest
function.
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
ntree = 500
Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.
keep.forest = TRUE
If TRUE
, forest is stored, which is
required for prediction.
nodesize = 5
Minimum number of observations in terminal nodes.
...
Other parameters passed to
randomForest
.
Other Learners:
Custom_chain
,
Lrnr_HarmonicReg
,
Lrnr_arima
,
Lrnr_bartMachine
,
Lrnr_base
,
Lrnr_bayesglm
,
Lrnr_bilstm
,
Lrnr_caret
,
Lrnr_cv_selector
,
Lrnr_cv
,
Lrnr_dbarts
,
Lrnr_define_interactions
,
Lrnr_density_discretize
,
Lrnr_density_hse
,
Lrnr_density_semiparametric
,
Lrnr_earth
,
Lrnr_expSmooth
,
Lrnr_gam
,
Lrnr_ga
,
Lrnr_gbm
,
Lrnr_glm_fast
,
Lrnr_glmnet
,
Lrnr_glm
,
Lrnr_grf
,
Lrnr_gru_keras
,
Lrnr_gts
,
Lrnr_h2o_grid
,
Lrnr_hal9001
,
Lrnr_haldensify
,
Lrnr_hts
,
Lrnr_independent_binomial
,
Lrnr_lightgbm
,
Lrnr_lstm_keras
,
Lrnr_mean
,
Lrnr_multiple_ts
,
Lrnr_multivariate
,
Lrnr_nnet
,
Lrnr_nnls
,
Lrnr_optim
,
Lrnr_pca
,
Lrnr_pkg_SuperLearner
,
Lrnr_polspline
,
Lrnr_pooled_hazards
,
Lrnr_ranger
,
Lrnr_revere_task
,
Lrnr_rpart
,
Lrnr_rugarch
,
Lrnr_screener_augment
,
Lrnr_screener_coefs
,
Lrnr_screener_correlation
,
Lrnr_screener_importance
,
Lrnr_sl
,
Lrnr_solnp_density
,
Lrnr_solnp
,
Lrnr_stratified
,
Lrnr_subset_covariates
,
Lrnr_svm
,
Lrnr_tsDyn
,
Lrnr_ts_weights
,
Lrnr_xgboost
,
Pipeline
,
Stack
,
define_h2o_X()
,
undocumented_learner
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