# Univariate feature ranking in classification

Scikit-learn has function to evaluate the F-statistics for univariate feature importance feature selection. According to the web page they are calculating ANOVA F value.

If I understood correctly, the univariate feature importance checks how important a specific feature is to the target without taking into consideration the other features. If I have the features $f_1,f_2,f_3,...f_n$, the univariate feature importance will check how well $f_1$ fits into the target, $f_2$ fits with the target,... $f_n$ fits into the target?

Why do we use ANOVA F when there are only two variables that are checked against each other?

I think I need some basic background here to understand the use of ANOVA F when doing univariate feature selection.

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