Multi-objective parameter selection for classifiers

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 conicting - criteria. the tunepareto package provides a general and highly customizable framework to select optimal parameters for classifiers according to multiple objectives. several strategies for sampling and optimizing parameters are supplied. the algorithm determines a set of pareto-optimal parameter configurations and leaves the ultimate decision on the weighting of objectives to the researcher. decision support is provided by novel visualization techniques.