MultivariateRandomForest (version 1.1.5)

CrossValidation: Generate training and testing samples for cross validation

Description

Generates Cross Validation Input Matrices and Output Vectors for training and testing, where number of folds in cross validation is user defined.

Usage

CrossValidation(X, Y, F)

Arguments

X

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

Y

output responses as column vector

F

Number of Folds

Value

List with the following components:

TrainingData

List of matrices where each matrix contains a fold of Cross Validation Training Data, where the number of matrices is equal to F

TestingData

List of matrices where each matrix contains a fold of Cross Validation Testing Data, where the number of matrices is equal to F

OutputTrain

List of matrices where each matrix contains a fold of Cross Validation Training Output Feature Data, where the number of matrices is equal to F

OutputTest

List of matrices where each matrix contains a fold of Cross Validation Testing Output Feature Data, where the number of matrices is equal to F

FoldedIndex

Index of Different Folds. (e.g., for Sample Index 1:6 and 3 fold, FoldedIndex are [1 2 3 4], [1 2 5 6], [3 4 5 6])