I have an sklearn pipeline consisting of a TfidfVectorizer and a SGDClassifier(loss='log'), fitted on a multi-label training dataset. When I then use ELI5 explain_prediction on a sample (using the vectorizer and classifier from pipeline), I get different probabilities than when I use pipeline.predict_proba on the same sample. Why is this?
Why do ELI5 probabilities differ from predict_proba
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