Cross-validation in RapidMiner

Posted by Armando Brito Mendes | Filed under software

Explica como utilizar a validação cruzada no RapidMiner

Explica como utilizar a validação cruzada no RapidMiner

Cross-validation is a standard statistical method to estimate the generalization error of a predictive model. In k-fold cross-validation a training set is divided into k equal-sized subsets. Then the following procedure is repeated for each subset: a model is built using the other (k - 1) subsets as the training set and its performance is evaluated on the current subset. This means that each subset is used for testing exactly once. The result of the cross-validation is the average of the performances obtained from the k rounds.

This post explains how to interpret cross-validation results in RapidMiner.

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Permalink | May 24th, 2013

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