splitt2: Split of the Parent node

In MultivariateRandomForest: Models Multivariate Cases Using Random Forests

Description

Split of the training samples of the parent node into the child nodes based on the feature and threshold that produces the minimum cost

Usage

splitt2(X, Y, m_feature, Index, Inv_Cov_Y, Command, ff)

Arguments

X	Input Training matrix of size M x N, M is the number of training samples and N is the number of features
Y	Output Training response of size M x T, M is the number of samples and T is the number of output responses
m_feature	Number of randomly selected features considered for a split in each regression tree node.
Index	Index of training samples
Inv_Cov_Y	Inverse of Covariance matrix of Output Response matrix for MRF (Input [0 0; 0 0] for RF)
Command	1 for univariate Regression Tree (corresponding to RF) and 2 for Multivariate Regression Tree (corresponding to MRF)
ff	Vector of m_feature from all features of X. This varies with each split

Details

At each node of a regression a tree, a fixed number of features (m_feature) are selected randomly to be considered for generating the split. Node cost for all selected features along with possible n-1 thresholds for n samples are considered to select the feature and threshold with minimum cost.

Value

List with the following components:

index_left	Index of the samples that are in the left node after splitting
index_right	Index of the samples that are in the right node after splitting
which_feature	The number of the feature that produces the minimum splitting cost
threshold_feature	The threshold value for the node split. A feature value less than or equal to the threshold will go to the left node and it will go to the right node otherwise.

Examples

library(MultivariateRandomForest)
X=matrix(runif(20*100),20,100)
Y=matrix(runif(20*3),20,3)
m_feature=5
Index=1:20
Inv_Cov_Y=solve(cov(Y))
ff2 = ncol(X) # number of features
ff =sort(sample(ff2, m_feature)) 
Command=2#MRF, as number of output feature is greater than 1
Split_criteria=splitt2(X,Y,m_feature,Index,Inv_Cov_Y,Command,ff) 

MultivariateRandomForest documentation built on May 2, 2019, 1:05 p.m.