split_node: Splitting Criteria of all the nodes of the tree

Description Usage Arguments Details Value

View source: R/split_node.R


Stores the Splitting criteria of all the nodes of a tree in a list


split_node(X, Y, m_feature, Index, i, model, min_leaf, Inv_Cov_Y, Command)



Input Training matrix of size M x N, M is the number of training samples and N is the number of features


Output Training response of size M x T, M is the number of samples and T is the number of output responses


Number of randomly selected features considered for a split in each regression tree node


Index of training samples


Number of split. Used as an index, which indicates where in the list the splitting criteria of this split will be stored.


A list of lists with the spliting criteria of all the node splits. In each iteration, a new list is included with the spliting criteria of the new split of a node.


Minimum number of samples in the leaf node. If a node has less than or, equal to min_leaf samples, then there will be no splitting in that node and the node is a leaf node. Valid input is a positive integer and less than or equal to M (number of training samples)


Inverse of Covariance matrix of Output Response matrix for MRF (Input [0 0; 0 0] for RF)


1 for univariate Regression Tree (corresponding to RF) and 2 for Multivariate Regression Tree (corresponding to MRF)


This function calculates the splitting criteria of a node and stores the information in a list format. If the node is a parent node, then indices of left and right nodes and feature number and threshold value of the feature for the split are stored. If the node is a leaf, the output feature matrix of the samples for the node are stored as a list.


model: A list of lists with the splitting criteria of all the split of the nodes. In each iteration, the Model is updated with a new list that includes the splitting criteria of the new split of a node.

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