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Filter or screening methods are often used as a preprocessing step forreducing the number of variables used by a learning algorithm in obtaining aclassification or regression model. While there are many such filter methods,there is a need for an objective evaluation of these methods. Such anevaluation is needed to compare them with each other and also to answer whetherthey are at all useful, or a learning algorithm could do a better job withoutthem. For this purpose, many popular screening methods are partnered in thispaper with three regression learners and five classification learners andevaluated on ten real datasets to obtain accuracy criteria such as R-square andarea under the ROC curve (AUC). The obtained results are compared through curveplots and comparison tables in order to find out whether screening methods helpimprove the performance of learning algorithms and how they fare with eachother. Our findings revealed that the screening methods were useful inimproving the prediction of the best learner on two regression and twoclassification datasets out of the ten datasets evaluated.

Are screening methods useful in feature selection? An empirical study