cvSplit¶
Purpose¶
Returns the test and training set for the ith of k cross validation splits for a given set of dependent and independent variables.
Format¶

{ y_train, y_test, X_train, X_test } =
cvSplit
(y, X, k, i)¶ Parameters:  y (Nx1 vector, or NxK matrix.) – The dependent variable(s).
 X (Nx1 vector, or NxK matrix) – The independent variable(s).
 k (Scalar) – The number of folds.
 i (Scalar) – The fold number.
Returns:  y_train – The training target values for the ith CV split.
 y_test – The test target values for the ith CV split
 X_train – The training predictor values for the ith CV split.
 X_test – The test predictor values for the ith CV split.
Examples¶
y = { 7, 2, 5, 1, 3, 4 };
X = { 1 3,
9 6,
6 1,
8 4,
9 5,
1 8 };
// Divide the dataset into 3 folds. Place the first
// 1/3 of the observations in the test set and the remaining
// observations in the training set.
{ y_train, y_test, X_train, X_test } = cvSplit(y, X, 3, 1);
After the above code:
y_train = 5 X_train = 6 1
1 8 4
3 9 5
4 1 8
y_test = 7 X_test = 1 3
2 9 6
Continuing with the same y and X from above, if we run:
// Divide the dataset into 3 folds. Place the second
// 1/3 of the observations in the test set and the remaining
// observations in the training set.
{ y_train, y_test, X_train, X_test } = cvSplit(y, X, 3, 2);
This time, the variables are assigned as follows:
y_train = 7 X_train = 1 3
2 9 6
3 9 5
4 1 8
y_test = 5 X_test = 6 1
1 8 4
Remarks¶
The observations from X and y are NOT randomly shuffled.
See also
Functions rndi()
, sampleData()
, trainTestSplit()