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In the research of classification task of machine learning,it is important for correctly evaluating the performance of the learning algorithm.In practical application,many performance measure indexes are proposed based on different perspectives.Three kinds of performance measure indexes based on error rate,confusion matrix and statistical test are introduced in this paper.The background,significance and scope of each measure index are discussed.The differences of different methods are analyzed.The future research problems and directions are also put forward and analyzed.Furthermore,the differences of these performance measure indexes are also compared by experimental data in portrait and landscape.The consistency of these performance measure indexes is also analyzed in classification algorithm selection.