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The Attribute Selected Classifier

Experimenting with a dataset to select attributes and applying a classifier to the result risks cheating! Ian Witten explains what to do about it.

Experimenting with a dataset to select attributes and applying a classifier to the result is cheating, if performance is evaluated using cross-validation, because the entire dataset is used to determine the attribute subset. You mustn’t use the test data when setting discretization boundaries! But with cross-validation you don’t really have an opportunity to use the training data only. Enter the FilteredClassifier, which solves the problem. (Does that ring a bell? You saw it before, in Week 2.)

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