Training indices for cross-validation
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Syntax
idx = training(c)
idx = training(c,i)
idx = training(c,"all")
Description
example
idx = training(c) returns the training indices idx for a cvpartition object c of type 'holdout' or 'resubstitution'.
If
c.Typeis'holdout', thenidxspecifies the observations in the training set.If
c.Typeis'resubstitution', thenidxspecifies all observations.
idx = training(c,i) returns the training indices for the repetitions specified in i of a cvpartition object c of type 'kfold' or 'leaveout'.
If
c.Typeis'kfold', thenidx(:,j)specifies the observations in training seti(j).If
c.Typeis'leaveout', thenidx(:,j)specifies the observations reserved for training at repetitioni(j).
example
idx = training(c,"all") returns the training indices for all repetitions of a cvpartition object c. Column j in idx indicates the observations in training set j for an object of type 'kfold' or 'leaveout'. (since R2023b)
Examples
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Identify Training Indices in Holdout Partition
Open Live Script
Identify the observations that are in the training set of a cvpartition object for holdout validation.
Partition 10 observations for holdout validation. Select approximately 30% of the observations to be in the test (holdout) set.
rng('default') % For reproducibilityc = cvpartition(10,'Holdout',0.30)
c = Hold-out cross validation partition NumObservations: 10 NumTestSets: 1 TrainSize: 7 TestSize: 3 IsCustom: 0
Identify the training set observations. Observations that correspond to 1s are in the training set.
set = training(c)
set = 10x1 logical array 1 1 1 0 1 1 1 1 0 0Visualize the results. All observations except the fourth, ninth, and tenth are in the training set.
h = heatmap(double(set),'ColorbarVisible','off');sorty(h,'1','ascend')ylabel('Observation')title('Training Set Observations')
Identify Training Indices in k-Fold Partition
Open Live Script
Identify the observations that are in the training sets 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("default") % For reproducibilityc = cvpartition(10,"KFold",3)
c = K-fold cross validation partition NumObservations: 10 NumTestSets: 3 TrainSize: 7 6 7 TestSize: 3 4 3 IsCustom: 0
Identify the training set observations for each repetition of training and test data. Observations that correspond to 1s are in the corresponding training set.
data = training(c,"all")data = 10x3 logical array 0 1 1 0 1 1 1 0 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 0 1 1 1 0 1Visualize the results. All observations except the first, second, and ninth are in the first training set. All observations except the third, sixth, eighth, and tenth are in the second training set. All observations except the fourth, fifth, and seventh are in the third training set.
h = heatmap(double(data),"ColorbarVisible","off");sorty(h,["1","2","3"],"ascend")xlabel("Repetition")ylabel("Observation")title("Training Set Observations")
Input Arguments
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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 training set of the corresponding repetition.
Example: 2
Example: [1 3 5]
Data Types: single | double
Output Arguments
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idx — Indices for training set observations
logical matrix
Indices for training 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 training set. A value of 0 (false) indicates that the corresponding observation is in the test set.
Version History
Introduced in R2008a
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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 each1(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 each1(true) entry indicates a training set observation.
See Also
cvpartition | test
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