CrossValidation: Generate training and testing samples for cross validation

In IntegratedMRF: Integrated Prediction using Uni-Variate and Multivariate Random Forests

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 response 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])

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