SelectKBest(f_classif, k), where
k is the number of features to select, is often used for feature selection, however, I am having trouble finding descriptive documentation on how it works. A sample of how this works is below:
model = SelectKBest(f_classif, k) model.fit_transform(X_train, Target_train)
The ANOVA F-value, as I understand it, does not require a categorical response. (see
scipy.stats.f_oneway) It is computing the value between the features. Why does
f_classif require the response?
SelectKBest actually achieve a ranking of features based on the F-value when there should only be one F-value for a set of data?