build_mrf: Train Multivariate Random Forest

In dpweix/mlmcusum: Detects Changes in Covariance for Multivariate TS

Train Multivariate Random Forest

Description

Similar to the build_forest_predict function from MultivariateRandomForest. However, this function will save the model for future use.

Usage

build_mrf(trainX, trainY, n_tree, m_feature, min_leaf)

Arguments

trainX	The design matrix for predictions. Can be made with create_X.
trainY	The value of the response variables.
n_tree	Number of trees in the forest, which must be positive integer.
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. If a node has less than or equal to min_leaf samples, then there will be no splitting in that node and this node will be considered as a leaf node. Valid input is positive integer, which is less than or equal to M (number of training samples)

Value

A named list including the trained model and it's predictions.

dpweix/mlmcusum documentation built on July 31, 2023, 10:13 a.m.