Usage
split_node(X, Y, m_feature, Index, i, model, min_leaf, Inv_Cov_Y, Command)
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
i
Number of split. Used as an index, which indicates where in the list the splitting
criteria of this split will be stored.
model
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.
min_leaf
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)
Inv_Cov_Y
Inverse of Covariance matrix of Output Response matrix for MRF(Give Zero for RF)
Command
1 for univariate Regression Tree (corresponding to RF) and 2 for Multivariate Regression Tree (corresponding to MRF)