Usage
split_node(X, Y, mtree, Index, i, model, min_leaf, V_inv, Command)
Arguments
X
Input Training matrix of M x N, M is the number of training samples and N is the number of features
Y
Output Training response of M x T, M is the number of samples and T is number of ouput Features(Response)
mtree
Number of randomly selected features used for each split
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 split of the nodes. 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 equal to min_leaf samples,
then there will be no splitting in that node and this node is a leaf node.
V_inv
Covariance matrix of Output Feature matrix
Command
1 for RF and 2 for MRF depending on the method