test

Test indices for cross-validation

Syntax

idx = test(c)

idx = test(c,i)

idx = test(c,"all")

Description

idx = test(c) returns the test indices idx for a cvpartition object c of type 'holdout' or 'resubstitution'.

• If c.Type is 'holdout', then idx specifies the observations in the test set.

• If c.Type is 'resubstitution', then idx specifies all observations.

idx = test(c,i) returns the test indices for the repetitions specified in i of a cvpartition object c of type 'kfold' or 'leaveout'.

• If c.Type is 'kfold', then idx(:,j) specifies the observations in test set i(j).

• If c.Type is 'leaveout', then idx(:,j) specifies the observation reserved for testing at repetition i(j).

idx = test(c,"all") returns the test indices for all repetitions of a cvpartition object c. Column j in idx indicates the observations in test set j for an object of type 'kfold' or 'leaveout'. (since R2023b)

Examples

Identify Test Indices in Holdout Partition

Identify the observations that are in the test (holdout) set of a cvpartition object.

Partition 10 observations for holdout validation. Select approximately 30% of the observations to be in the test set.

rng(0,"twister") % For reproducibility
c = cvpartition(10,Holdout=0.30)

c = 
Hold-out cross validation partition
    NumObservations: 10
        NumTestSets: 1
          TrainSize: 7
           TestSize: 3
           IsCustom: 0
          IsGrouped: 0
       IsStratified: 0


  Properties, Methods

Identify the test set observations. Observations that correspond to 1s are in the test set.

holdout = test(c)

holdout = 10×1 logical array

   0
   0
   0
   1
   0
   0
   0
   0
   1
   1

Visualize the results. The fourth, ninth, and tenth observations are in the test set.

h = heatmap(double(holdout),ColorbarVisible="off");
sorty(h,"1","descend")
ylabel("Observation")
title("Test Set Observations")

Identify Test Indices in k-Fold Partition

Identify the observations that are in the test sets, or folds, of a cvpartition object for 3-fold cross-validation.

Partition 10 observations for 3-fold cross-validation. Notice that c contains three repetitions of training and test data.

rng(0,"twister") % For reproducibility
c = cvpartition(10,KFold=3)

c = 
K-fold cross validation partition
    NumObservations: 10
        NumTestSets: 3
          TrainSize: [7 6 7]
           TestSize: [3 4 3]
           IsCustom: 0
          IsGrouped: 0
       IsStratified: 0


  Properties, Methods

Identify the test set observations for each repetition of training and test data. Observations that correspond to 1s are in the corresponding test set (fold).

data = test(c,"all")

data = 10×3 logical array

   1   0   0
   1   0   0
   0   1   0
   0   0   1
   0   0   1
   0   1   0
   0   0   1
   0   1   0
   1   0   0
   0   1   0

Visualize the results. The first, second, and ninth observations are in the first test set. The third, sixth, eighth, and tenth observations are in the second test set. The fourth, fifth, and seventh observations are in the third test set.

h = heatmap(double(data),ColorbarVisible="off");
sorty(h,["1","2","3"],"descend")
xlabel("Repetition")
ylabel("Observation")
title("Test Set Observations")

Input Arguments

c — Validation partition

cvpartition object

Validation partition, specified as a cvpartition object. The validation partition type of c, c.Type, is 'kfold', 'holdout', 'leaveout', or 'resubstitution'.

i — Repetition indices

positive integer vector

Repetition indices, specified as a positive integer vector. For each element in i, the software finds the observations in the test set (fold) of the corresponding repetition.

Example: 1

Example: [2 4 6 8 10]

Data Types: single | double

Output Arguments

idx — Indices for test set observations

logical matrix

Indices for test set observations, returned as a logical matrix. Each row corresponds to an observation, and each column corresponds to a repetition.

A value of 1 (true) indicates that the corresponding observation is in the test set. A value of 0 (false) indicates that the corresponding observation is in the training set.

Version History

Introduced in R2008a

R2023b: Retrieve indices for multiple training or test sets simultaneously

For both custom and noncustom cvpartition objects, you can return multiple training set indices or multiple test set indices simultaneously by using the training or test function, respectively. For example, for a 5-fold cross-validation cvpartition object c:

• The syntax test(c,[1 3 5]) returns a logical array with 3 columns, where each 1 (true) entry indicates a test set observation. The three columns correspond to the first, third, and fifth repetitions.

• The syntax training(c,"all") returns a logical array with 5 columns, where each 1 (true) entry indicates a training set observation.