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Respected professor, your videos are guiding me very well and recently I'm learning how to select features. I have one huge confusion, Anova(F-test) is often used in Filter method for feature selection. Theory says, Anova should be used for feature selection when target is Binary but I saw in some practical use people also uses Anova when target is multi class. So Anova(F-test) can also be applied if our target is not binary and has multiple classes(say data like IRIS)? Another question Anova assumes features to be normally distributed, But in practice most of the time we encounter data that are not fully normal in such case does it matter much to apply it in feature selection? or Transformation of data into some distribution is compulsory? Please clear my confusion answering these two questions.
Hi Nice informative video about feature selection, i have a question regarding feature selection when we have more than one target variable, i.e. in case of MultiOutput regression problem how to perform feature selection?
Got a question on the topic? Please share it in the comment section below. For Edureka’s Data Science Masters Certification Training, Please Visit our Website bit.ly/3glo1jb Use code "TH-cam20" to get Flat 20% off on this training.
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Thanks for the content , confusions are got cleared after this wonderful Explanation.
We are very glad to hear that your a learning well with our contents :) continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
So satisfying!! Thanks for this amazing tutorial!!
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Respected professor, your videos are guiding me very well and recently I'm learning how to select features. I have one huge confusion, Anova(F-test) is often used in Filter method for feature selection. Theory says, Anova should be used for feature selection when target is Binary but I saw in some practical use people also uses Anova when target is multi class. So Anova(F-test) can also be applied if our target is not binary and has multiple classes(say data like IRIS)?
Another question Anova assumes features to be normally distributed, But in practice most of the time we encounter data that are not fully normal in such case does it matter much to apply it in feature selection? or Transformation of data into some distribution is compulsory? Please clear my confusion answering these two questions.
Hi Nice informative video about feature selection, i have a question regarding feature selection when we have more than one target variable, i.e. in case of MultiOutput regression problem how to perform feature selection?