I'm trying to run a quick univariate filtering on some data, using a t-test of independence, since my target is binary. However, when I run the filter using sklearn's SelectKBest, I get the same top features returned doing a manual filter, but in different order. The only information about SelectKBest I could find is here and the documentation, but both seem like they should work like my manual method. My code is

import numpy as np
from sklearn.feature_selection import SelectKBest
from scipy.stats import ttest_ind


data = np.random.random((100,50))
target = np.random.randint(2, size = 100).reshape((100,1))

X = data
y = target.ravel()

k = 10
p_values = []
for i in range(data.shape[1]):

    t, p = ttest_ind(data[:,i], target)

p_values = sorted(p_values, key = lambda x: x[1])
p_values = p_values[:k]

# Indices of the ranked p-values
idx = [i[0] for i in p_values]

# SelectKBest features
mdl = SelectKBest(ttest_ind, k = k)

X_new = mdl.fit_transform(X, y)
# Manually selected k best features

# Print first row of sklearn features
array([0.4236548 , 0.96366276, 0.38344152, 0.87001215, 0.63992102,
   0.52184832, 0.41466194, 0.06022547, 0.67063787, 0.31542835])

# Print first row of manually selected features
array([0.67063787, 0.4236548 , 0.31542835, 0.87001215, 0.38344152,
   0.63992102, 0.06022547, 0.52184832, 0.41466194, 0.96366276])

It seems SelectKBest is not ordering the features based solely on their p-values or their t-values. How does SelectKBest order the features then?


1 Answer 1


No, SelectKBest and other *Select* transformers from sklearn.feature_selection do not change order of features, only drop not selected ones. Anyway, generally, machine learning models do not utilize relative order of a feature.

If you need to check and reorder features, you can use scores_ and/or pvalues_ attributes of a fitted transformer (e.g. SelectKBest) object.

Under the hood those classes use a boolean mask to identify which features to keep. The boolean mask is composed using the first (or the only) set of scores. Those would be t-values in your case. p-values are not used by SelectKBest to select features for now, according to the source code.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

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