build_single_tree: Model of a single tree of Random Forest or Multivariate...

In IntegratedMRF: Integrated Prediction using Uni-Variate and Multivariate Random Forests

Description

Build a Univariate Regression Tree (for generation of Random Forest (RF) ) or Multivariate Regression Tree ( for generation of Multivariate Random Forest (MRF) ) using the training samples, which is used for the prediction of testing samples.

Usage

build_single_tree(X, Y, m_feature, min_leaf, Inv_Cov_Y, Command)

Arguments

X	Input Feature matrix of M x N, M is the number of training samples and N is the number of input features
Y	Output Feature matrix of M x T, M is the number of training samples and T is the number of ouput features
m_feature	Number of randomly selected features considered for a split in each regression tree node, which must be positive integer and less than N (number of input features)
min_leaf	Minimum number of samples in the leaf node, which must be positive integer and less than or equal to M (number 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)

Details

The regression tree structure is represented as a list of lists. For a non-leaf node, it contains the splitting criteria (feature for split and threshold) and for a leaf node, it contains the output responses for the samples contained in the leaf node.

Value

Model of a single regression tree (Univariate or Multivariate Regression Tree). An example of the list of the non-leaf node:

Flag for determining node status; leaf node (1) or branch node (0)	1
Index of samples for the left node	int [1:34] 1 2 4 5 ...
Index of samples for the right node	int [1:16] 3 6 9 ...
Feature for split	int 34
Threshold values for split, average them	num [1:3] 0.655 0.526 0.785
List number for the left and right nodes	num [1:2] 2 3

An example of the list of the leaf node:

Output responses

num[1:4,1:5] 0.0724 0.1809 0.0699 ...

IntegratedMRF documentation built on May 2, 2019, 2:15 a.m.