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Understanding f_classif in sklearn

Recently, to get better features, I used SelectKBest (score_func =, k = 20). f_classif computes anova f-value between function for classification problems. I've used it and got the best results. I found out that anova f-test calculates the relationship "between class variance" to "within class variance". But I have the following questions:

1) Does f_classif use a combination of functions to give an f-score ?. 2) Can I get pseudocode on how the SelectKBest function works? 3) How does f_classif work in sklearn ?. Thank you in advance.

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This is a bit late and my answer only answers one of the questions you asked: f_classif is executed on a function-by-function basis, that is, individually. So there is no combination of features.



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