RandomEAT: Individual EAT for Random Forest

In MiriamEsteve/EAT: Efficiency Analysis Trees

Description

This function builds an individual tree for Random Forest

Usage

RandomEAT(data, x, y, numStop, s_mtry)

Arguments

data	Dataframe containing the training set.
x	Vector. Column input indexes in data.
y	Vector. Column output indexes in data.
numStop	Integer. Minimum number of observations in a node for a split to be attempted.
s_mtry	Number of variables randomly sampled as candidates at each split. The available options are: "BRM", "DEA1", "DEA2", "DEA3", "DEA4" or any integer.

Value

List of m trees in forest and the error that will be used in the ranking of the importance of the variables.

MiriamEsteve/EAT documentation built on Jan. 18, 2022, 6:55 p.m.