A variety of measures exist to assess the accuracy of predictive models in data mining and several aspects should be considered when evaluating the performance of learning algorithms. In this article, the most common accuracy and error scores for classification and regression are reviewed and compared. Moreover, the standard approaches to model selection and assessment are presented, together with an introduction to ensemble methods for improving the accuracy of single classifiers.
Data mining: Accuracy and error measures for classification and prediction
Galdi P.;Tagliaferri R.
2018-01-01
Abstract
A variety of measures exist to assess the accuracy of predictive models in data mining and several aspects should be considered when evaluating the performance of learning algorithms. In this article, the most common accuracy and error scores for classification and regression are reviewed and compared. Moreover, the standard approaches to model selection and assessment are presented, together with an introduction to ensemble methods for improving the accuracy of single classifiers.File in questo prodotto:
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