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In RapidMiner I want to create a k-NN model in order to create a classifier. To generate the test sets and the training sets I use the the cross-validation.

If I choose 10 as the number of folds, the cross-validation algorithm will do 10 iterations with different test sets and training sets.

At the end, what training set will be used to create my k-NN model ?

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  • $\begingroup$ The classifier is trained with the whole dataset. The cross-validation folding is done in order to have a statistical measure of the "quality" of the model, For instance, you set K=3 and some certain distance, then, with the 10-fold-CV you have an average accuracy of the model given these parameters, and the final classifier should be one which has been trained with the whole training set. $\endgroup$
    – ignatius
    May 16, 2018 at 8:51

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Apart from having an average accuracy of the trained model, also you can use cross-validation to approximate the optimal value for the k parameter and the optimal metric.

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  • $\begingroup$ This generalises to the more common use of cross-validation to tune algorithm hyperparameters. $\endgroup$
    – bradS
    May 16, 2018 at 13:21

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