I think I understand the basic definition of p-values in statistics, but I'm confused what it would mean in the context of feature selection. For example, in scikit-learn you can do feature selection with the SelectPercentile class and tell it to, say, only keep the top 30 percent of features.
SelectPercentile class has two attributes:
pvalues_. I assume to get the top 30 percent of features it just ranks them by their scores and takes the top 30 percent. But what would a pvalue mean in this context?
For reference, I ran some of my data through SelectPercentile and I got the following:
Feature scores: array([ 71.63040161, 5156.66259766, 1368.79492188, 805.26611328, 788.79217529, 110.83755493, 705.46398926, 854.82958984], dtype=float32) Feature pvalues: array([ 2.97808976e-17, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 8.89241668e-26, 0.00000000e+00, 0.00000000e+00], dtype=float32)