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

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The Notebook: colab.research.google.com/dri...
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Опубликовано:

20 окт 2021

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Комментарии : 15

@GregHogg 11 месяцев назад

Take my courses at mlnow.ai/!

@Alphamusclenerd Год назад

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 Год назад

Haha thank you I appreciate that!!

@jacobdavies3761 4 месяца назад

Cheers for the help mate 👍

@prachishah2151 2 года назад

Thank you so much for the amazing video

@GregHogg 2 года назад

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

@arsheyajain7055 2 года назад

LOVE THE THUMBNAIL

@GregHogg 2 года назад

Thank you!!!

@sukanyaacharya5202 2 года назад

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 2 года назад

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

@panagiotisgoulas8539 2 года назад

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 2 года назад

Thanks Panagiotis, I appreciate that!!