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Predict response for observations not used for training


label = kfoldPredict(obj)
[label,score] = kfoldPredict(obj)
[label,score,cost] = kfoldPredict(obj)


label = kfoldPredict(obj) returns class labels predicted by obj, a cross-validated classification. For every fold, kfoldPredict predicts class labels for in-fold observations using a model trained on out-of-fold observations.

[label,score] = kfoldPredict(obj) returns the predicted classification scores for in-fold observations using a model trained on out-of-fold observations.

[label,score,cost] = kfoldPredict(obj) returns misclassification costs.

Output Arguments


Vector of class labels of the same type as the response data used in training obj. (The software treats string arrays as cell arrays of character vectors.) Each entry of label corresponds to a predicted class label for the corresponding row of X.


Numeric matrix of size N-by-K, where N is the number of observations (rows) in obj.X, and K is the number of classes (in obj.ClassNames). score(i,j) represents the confidence that row i of obj.X is of class j. For details, see More About.


Numeric matrix of misclassification costs of size N-by-K. cost(i,j) is the average misclassification cost of predicting that row i of obj.X is of class j.


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Find the cross-validation predictions for a model based on Fisher's iris data.

Load Fisher's iris data set.

load fisheriris

Train an ensemble of classification trees using AdaBoostM2. Specify tree stumps as the weak learners.

rng(1); % For reproducibility
t = templateTree('MaxNumSplits',1);
Mdl = fitcensemble(meas,species,'Method','AdaBoostM2','Learners',t);

Cross validate the trained ensemble using 10-fold cross validation.

CVMdl = crossval(Mdl);

Estimate cross-validation predicted labels and scores.

[elabel,escore] = kfoldPredict(CVMdl);

Display the maximum and minimum scores of each class.

ans = 1×3

    9.3862    8.9871   10.1866

ans = 1×3

    0.0018    3.8359    0.9573

Create a confusion matrix using the 10-fold cross-validation predictions of a discriminant analysis model.

Load the fisheriris data set. X contains flower measurements for 150 different flowers, and y lists the species, or class, for each flower. Create a variable order that specifies the order of the classes.

load fisheriris
X = meas;
y = species;
order = unique(y)
order = 3x1 cell
    {'setosa'    }
    {'virginica' }

Create a 10-fold cross-validated discriminant analysis model by using the fitcdiscr function. By default, fitcdiscr ensures that training and test sets have roughly the same proportions of flower species. Specify the order of the flower classes.

cvmdl = fitcdiscr(X,y,'KFold',10,'ClassNames',order);

Predict the species of the test set flowers.

predictedSpecies = kfoldPredict(cvmdl);

Create a confusion matrix that compares the true class values to the predicted class values.


More About

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