Score of importance from feature selection techniques Can I get the score of importance for each feature in feature selection methos such as Chi2, Information Gain (IG), or Recursive Feature Elimination (RFE)? Or they just provide a list of important features?
 A: As far as I know, in scikit-learn you can only get individual (relative) features importance if you're using an ensemble method of decision trees. After fitting a model you can check model.feature_importances_. As in the example below:
# Feature Importance
from sklearn import datasets
from sklearn import metrics
from sklearn.ensemble import ExtraTreesClassifier
# load the iris datasets
dataset = datasets.load_iris()
# fit an Extra Trees model to the data
model = ExtraTreesClassifier()
model.fit(dataset.data, dataset.target)
# display the relative importance of each attribute
print(model.feature_importances_)

Source: https://machinelearningmastery.com/feature-selection-in-python-with-scikit-learn/
This question has also some more in-depth answers on the subject.
A: I don't use scikit-learn so I can't speak to the specifics on that, but the R package CORElearn will give you importance values (usually on [0,1] I think) for features using information gain, minimum description length, and a whole host of others.  Might be of interest to check that package out if scikit-learn can't provide what you are seeking.
