ML.H2O.randomForest: ML.H2O.randomForest

In frbl/OnlineSuperLearner: Online SuperLearner package

Description

Wrapper for an H2O randomForest estimator. From their discription: Distributed Random Forest (DRF) is a powerful classification and regression tool. When given a set of data, DRF generates a forest of classification (or regression) trees, rather than a single classification (or regression) tree. Each of these trees is a weak learner built on a subset of rows and columns. More trees will reduce the variance. Both classification and regression take the average prediction over all of their trees to make a final prediction, whether predicting for a class or numeric value (note: for a categorical response column, DRF maps factors (e.g. 'dog', 'cat', 'mouse) in lexicographic order to a name lookup array with integer indices (e.g. 'cat -> 0, 'dog' -> 1, 'mouse' -> 2).

Usage

ML.H2O.randomForest

Format

An object of class R6ClassGenerator of length 24.

Methods

initialize(ntrees=50, min_rows=9)

Creates a new randomforest / drf model

@param nfolds integer (default = 0) specify the number of folds for cross-validation.

@param ntrees integer (default = 50) specify the number of trees to build.

@param min_rows (default = 9) specify the minimum number of observations for a leaf (nodesize in R).

@param verbose (default = FALSE) the verbosity of the fitting procedure

frbl/OnlineSuperLearner documentation built on Feb. 9, 2020, 9:28 p.m.