What do you need to know about the McNemar test?
What is the McNemar Test? The McNemar Test is a statistical test used to determine if the proportions of categories in two related groups significantly differ from each other. To use this test, you should have two group variables with two or more options.
How to calculate McNemar’s test to compare two machine learning classifiers?
The McNemar’s test statistic is calculated as: statistic = (Yes/No – No/Yes)^2 / (Yes/No + No/Yes) 1 statistic = (Yes/No – No/Yes)^2 / (Yes/No + No/Yes)
What is the null hypothesis of the McNemar test?
The null hypothesis is that there is no difference between recovery rates between the two measurements. Because our variable has two or more possible values (yes/no), and our two groups are paired (repeated measures from the same group), we know that the McNemar Test is appropriate to use. The analysis will result in a p-value.
When did Thomas Dietterich use the McNemar test?
In his widely cited 1998 paper, Thomas Dietterich recommended the McNemar’s test in those cases where it is expensive or impractical to train multiple copies of classifier models.
Is the Cochran’s Q test the same as the McNemar test?
The Cochran’s Q test is an extension of the McNemar’s test for more than two “treatments”. The Liddell’s exact test is an exact alternative to McNemar’s test. The Stuart–Maxwell test is different generalization of the McNemar test, used for testing marginal homogeneity in a square table with more than two rows/columns.
Which is more conservative exact binomial test or McNemar test?
However, simulations have shown both the exact binomial test and the McNemar test with continuity correction to be overly conservative. When b + c < 6, the exact-P-value always exceeds the common significance level 0.05. The original McNemar test was most powerful, but often slightly liberal.