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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?

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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.

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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.

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  • $\begingroup$ I don't think you are answering his question here, he is asking about specific feature importance algorithms not just if any exist. $\endgroup$ – astel Mar 11 at 19:37
  • $\begingroup$ @astel a matter of adverb. Fixed, thanks for speaking out. $\endgroup$ – Lucas Farias Mar 11 at 19:41

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