1
$\begingroup$

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.

The SelectPercentile class has two attributes: scores_ and 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)
$\endgroup$
1
$\begingroup$

It seems like SelectPercentile is able to select features according to different metrics of quality for the features. The specific metric used is determined by the score_func argument, which according to the documentation must be:

Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues) or a single array with scores. Default is f_classif (see below “See also”). The default function only works with classification tasks.

So by default it uses f_classif as a measure of features' quality. Looking at the documentation of this function you can see that it computes "the ANOVA F-value", so that is where your p-value comes from.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.