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I’m engaged on a personal project of prediction in 1vs1 sports activities. My neural community (MLP) have an precision of 65% (not wonderful but it really’s a superb start). I've 28 options And that i believe some influence my predictions. So I applied two algorithms mentionned in your post :

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I’m seeking to improve my Kaggle-kernel for the time being And that i wish to use characteristic assortment. Mainly because my supply facts incorporates NaN, I’m pressured to employ an imputer prior to the function range.

Create a product on each list of attributes and Review the performance of every. Look at ensembling the designs collectively to see if performance is often lifted.

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My books instruct you how to employ a library to work via a project end-to-conclusion and produce benefit, not just some tips

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Understand *args and **kwargs in Python three And the way they enable you to accept arbitrary amount of parameters 

Let us speak about how to obtain the notebooks for your training course along with the program substance. Check out the useful resource back links for this lecture!

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Did you accidently involve the class output variable in the information when accomplishing the PCA? It ought to be excluded.

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