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What is the difference between t-test and chi-square test?
A t-test tests a null hypothesis about two means; most often, it tests the hypothesis that two means are equal, or that the difference between them is zero. A chi-square test tests a null hypothesis about the relationship between two variables.
What’s the difference between chi-square and Anova?
A chi-square is only a nonparametric criterion. You can make comparisons for each characteristic. In Factorial ANOVA, you can investigate the dependence of a quantitative characteristic (dependent variable) on one or more qualitative characteristics (category predictors).
How to use Chi2 test for feature selection?
The SelectKBest function with the chi2 test only works with categorical data. In fact, the result from the test only will have real meaning if the feature only has 1’s and 0’s.
What does higher Chi2 mean in selectkbest?
So a higher value of chi2 statistic means “two categorical variables are dependent” and MORE USEFUL for classification. SelectKBest gives you the best two (k=2) features based on higher chi2 values. Thus you need to get those features that it gives, rather that getting the “other features” on the chi2 selector.
Which is the best way to conduct the Chi2 test?
Unweighted sample sizes (counts). Based on this, pure binary features can be easily summed up as counts, which is how people conduct the Chi2 test usually. But as long as the features are non-negative, one can always accumulated it in the contingency table in a “meaningful” way.
How is the chi square test useful in machine learning?
We always wonder where the Chi-Square test is useful in machine learning and how this test makes a difference. Feature selection is an important problem in machine learning, where we will be having several features in line and have to select the best features to build the model.