This learner provides fitting procedures for a faster implementation of
Random Forests, using the routines from ranger (described
in Wright and Ziegler (2017)
) through a call to the function
ranger. Variable importance functionality is also
provided through invocation of the importance method.
A learner object inheriting from Lrnr_base with
methods for training and prediction. For a full list of learner
functionality, see the complete documentation of Lrnr_base.
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
downstream prediction is not intended.
importance = "none": Variable importance mode, one of "none",
"impurity", "impurity_corrected", "permutation". The "impurity" measure
is the Gini index for classification, the variance of the responses for
regression, and the sum of test statistics (for survival analysis, see
the splitrule argument of ranger).
num.threads = 1: Number of threads.
...: Other parameters passed to ranger. See
its documentation for details.
Wright MN, Ziegler A (2017). “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software, 77(1), 1--17. doi: 10.18637/jss.v077.i01 .
Lrnr_randomForest for a similar learner using 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_randomForest,
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
data(mtcars)
# create task for prediction
mtcars_task <- sl3_Task$new(
data = mtcars,
covariates = c(
"cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "am",
"gear", "carb"
),
outcome = "mpg"
)
# initialization, training, and prediction with the defaults
ranger_lrnr <- Lrnr_ranger$new()
ranger_fit <- ranger_lrnr$train(mtcars_task)
ranger_preds <- ranger_fit$predict()
# variable importance
ranger_lrnr_importance <- Lrnr_ranger$new(importance = "impurity_corrected")
ranger_fit_importance <- ranger_lrnr_importance$train(mtcars_task)
ranger_importance <- ranger_fit_importance$importance()
# screening based on variable importance, example in glm pipeline
ranger_importance_screener <- Lrnr_screener_importance$new(
learner = ranger_lrnr_importance, num_screen = 3
)
glm_lrnr <- make_learner(Lrnr_glm)
ranger_screen_glm_pipe <- Pipeline$new(ranger_importance_screener, glm_lrnr)
ranger_screen_glm_pipe_fit <- ranger_screen_glm_pipe$train(mtcars_task)