Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms

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Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms (1998)

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by Thomas G. Dietterich

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713 - 8 self

BibTeX

@MISC{Dietterich98approximatestatistical,
    author = {Thomas G. Dietterich},
    title = { Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms},
    year = {1998}
}

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Abstract

This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I error). Two widely used statistical tests are shown to have high probability of type I error in certain situations and should never be used: a test for the difference of two proportions and a paired-differences t test based on taking several random train-test splits. A third test, a paired-differences t test based on 10-fold cross-validation, exhibits somewhat elevated probability of type I error. A fourth test, McNemar’s test, is shown to have low type I error. The fifth test is a new test, 5 × 2 cv, based on five iterations of twofold cross-validation. Experiments show that this test also has acceptable type I error. The article also measures the power (ability to detect algorithm differences when they do exist) of these tests. The cross-validated t test is the most powerful. The 5×2 cv test is shown to be slightly more powerful than McNemar’s test. The choice of the best test is determined by the computational cost of running the learning algorithm. For algorithms that can be executed only once, Mc-Nemar’s test is the only test with acceptable type I error. For algorithms that can be executed 10 times, the 5×2 cv test is recommended, because it is slightly more powerful and because it directly measures variation due to the choice of training set.

Keyphrases

approximate statistical test classification learning algorithm acceptable type cv test paired-differences test cross-validated test elevated probability certain situation new test high probability low type mcnemar test mc-nemar test twofold cross-validation fifth test algorithm difference fourth test 10-fold cross-validation computational cost used statistical test third test learning algorithm particular learning task several random train-test split

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