Lrnr_randomForest: Random Forests

In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Random Forests

Description

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

Format

R6Class object.

Value

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

Parameters

• 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 a terminal node.

• ...: Other parameters passed to randomForest.

See Also

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bayesglm, 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_glm_semiparametric, Lrnr_glmnet, Lrnr_glmtree, Lrnr_glm, Lrnr_grfcate, 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

Examples

data(cpp_imputed)
# create task for prediction
cpp_task <- sl3_Task$new(
  data = cpp_imputed,
  covariates = c("bmi", "parity", "mage", "sexn"),
  outcome = "haz"
)
# initialization, training, and prediction with the defaults
rf_lrnr <- Lrnr_randomForest$new()
rf_fit <- rf_lrnr$train(cpp_task)
rf_preds <- rf_fit$predict()

jeremyrcoyle/sl3 documentation built on Nov. 18, 2024, 4:21 p.m.