I'll assume you are following the workflow here: https://www.mathworks.com/help/stats/export-classification-model-for-use-with-new-data.html and running something like
[yfit,scores] = C.predictFcn(T)
The precise meaning of the scores depends on the type of classifier that you have trained. For most classifier types, the predicted class will correspond to the class with the highest score. However, the KNN, Naive Bayes, and Discrimant classifiers can be different when non-default misclassification costs are provided. For example, for a KNN classifier, the scores correspond to posterior probabilities which do not yet take misclassification costs into account. For a KNN classifier, the class with the minimum expected cost (rather than the maximum posterior probability score) will correspond to the predicted class. See https://www.mathworks.com/help/stats/classificationknn.predict.html , which indicates that the KNN predict function provides a third output which constains the expected cost after taking into account the misclassification costs:
% Example of predict function for KNN, with third output, the expected cost
[label,score,cost] = predict(mdl,X)
So, in the case of a KNN classifier, you will want to add a third output to your predict function in order to look at the expected costs.
If this doesn't answer your question, perhaps you can provide more details such as the classifier type that you trained and an example result.
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