what to do with correlated features? ought to We modify them to one thing new? a mixture probably? How can it influence our modeling and prediction? appreciated for those who immediate me into some resources to study and uncover it out.
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I’m seeking to enhance my Kaggle-kernel in the intervening time And that i would like to use characteristic collection. Simply because my source facts includes NaN, I’m compelled to work with an imputer before the aspect choice.
they are helpful illustrations, but i’m not sure they apply to my unique regression difficulty i’m looking to build some types for…and considering that I've a regression trouble, are there any feature selection solutions you may advise for steady output variable prediction?
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I have a dataset which incorporates each categorical and numerical capabilities. Must I do aspect variety just before one particular-warm encoding of categorical features or following that ?
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I attempted Element Worth process, but each of the values of variables are higher than 0.05, so will it signify that every one the variables have minor relation with the predicted worth?