K-Fold Cross Validation: Explanation + Tutorial in Python, Scikit-Learn & NumPy

The Notebook: colab.research...
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[@GregHogg]

Take my courses at mlnow.ai/!

[@SaffTechJourney]

I don't get why your videos don't have millions of views yet, you explain everything so clearly! awesome work man keep it up!

[@GregHogg]

Haha thank you I appreciate that!!

[@arsheyajain7055]

LOVE THE THUMBNAIL

[@GregHogg]

Thank you!!!

[@sukanyaacharya5202]

Hi Greg.. Thank you for the wonderful video. I have been working on Time series evaluation and specifically cross validation. My question is, is there a way to generalize the parameter tuning once the cross validation is complete or is it just trial and error basis.

[@GregHogg]

You can always tune values to minimize cross val loss :)

[@jacobdavies3761]

Cheers for the help mate 👍

[@panagiotisgoulas8539]

For anyone wondering why he flatten the images example: X_train.reshape(X_train.shape[0],-1) is because sklearn predict wants a 2D array as a first argument.

[@GregHogg]

Thanks Panagiotis, I appreciate that!!

[@prachishah2151]

Thank you so much for the amazing video

[@GregHogg]

Glad it was helpful and you're very welcome, Prachi!

[@pogiribhanuteja4650]

wanted an 'statistics for data science' video plz.. like distributions etc.,

[@GregHogg]

Sure!

[@bezelyesevenordek]

🤓🤓🤓🤓🤓🤓🤓🤓🤓🤓🤓🤓🤓🤓🤓🤓🤓