I am currently working on Career Guidance prediction using Machine Learning.
The dataset has 38 features. For feature selection I tried using mutual_info_classif for getting the mutual information of my features and got the list of important features.
The Second approach I followed is using SelectKBest with mutual_info_classif as my score_func. On this approach I got some other list of features.
Is it normal to get different results ?
Can anyone please help me out?
Hello,
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Hello @PriyeshDave-5166
Thanks for reaching out to us here. Based on my understanding, if you keep X, Y the same, and you are using sklearn.feature_selection.SelectKBest(score_func= mutual_info_classif , *, k= n) the result should be the same.
Are you manually rank the feature with mutual_info_classif score? The SelectKBest function is basically return the K highest feature based on the X and Y.
If this is not your case, I would highly recommend you to check with Scikit-learn team by email : https://mail.python.org/mailman/listinfo/scikit-learn
Or forum: https://stackoverflow.com/questions/tagged/scikit-learn
Hope this will help. Please let us know if any further queries.
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