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Multi-Objective Parameter Selection for Classifiers
2012
Journal of Statistical Software
Setting the free parameters of classifiers to different values can have a profound impact on their performance. For some methods, specialized tuning algorithms have been developed. These approaches mostly tune parameters according to a single criterion, such as the cross-validation error. However, it is sometimes desirable to obtain parameter values that optimize several concurrent -often conflicting -criteria. The TunePareto package provides a general and highly customizable framework to
doi:10.18637/jss.v046.i05
fatcat:mouyb6hvwnbnhetn7r75mhlpju