SoftForestPredDepth1: Building a single level for the Random Forest of SDTs.

In SoftRandomForest: Classification Random Forests for Soft Decision Trees

Description

Building a single level for the Random Forest of SDTs.

Usage

SoftForestPredDepth1(trainresponse, train, test, num.features, ntry,
  keep = FALSE)

Arguments

trainresponse	A vector of responses 0 and 1 for the training set with length equal to the number of observations in the training set.
train	A matrix or data frame consisting of all possible variables to attempt for the training set.
test	A matrix or data frame consisting of all possible variables to attempt for the test set.
num.features	The number of variables in the dataset to possibly try. The leftmost number of variables in the dataset are the variables chosen.
ntry	The number of variables from the num.features to attempt to split. This is useful for building random forests. For a standard tree, choose ntry = num.features.
keep	Logical if weights from a single observation should be kept. Keep FALSE if a Random Forest is to be built.

Value

A vector of the final fitted probabilities for this classification.

SoftRandomForest documentation built on May 15, 2019, 5:05 p.m.