K-folds do we still need to implement train_test_split?

Question

I've been reading quite a bit and i'm a little confused with k-folds. I understand the concept behind it, but i'm not sure about how to deploy it.

The usual step that i've been seeing after data exploration is train_test_split, encoding and scaling fit_transform the training sets and just fitting the testing sets before testing which algorithms work. After which they tune the hyper-parameters.

So if I were to use k-folds now, do I avoid using train_test_split? And at which do we use k-folds?

Thanks!

Batuhan B · Accepted Answer · 2020-01-20T15:06:28.913

1

No. K-fold splits your data into train-test split K times so you train K different models.

This approach makes your model results more robust because you train K different models with different parts of your dataset and also you predict different parts of your data K times. Finally, you can simply take the average score of K model.

edited Jan 20 '20 at 15:06

answered Jan 20 '20 at 15:05

Batuhan B

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hi Batuhan, so right after data explodation, we can implement k-folds am i right? – Jonathan Jan 20 '20 at 15:06
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Yes you can. Also don't forget to make normalization on your dataset before splitting. – Batuhan B Jan 20 '20 at 15:07

K-folds do we still need to implement train_test_split?

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