5 Powerful Cross-Validation Methods to Skyrocket Robustness of Your ML Models

Author(s): Bex T.

Originally published on Towards AI.

All CV procedures you need to know as a data scientist, explained

Image by me with Midjourney

Before I start selling the merchandise, I need to pitch the main idea. Picture a crazy world where you don’t know what cross-validation is. In this world, you split your data into a single train and test set, train your model, and test it. If unsatisfied with the score, you tweak your model until GridSearch (or Optuna) cries out “enough!”.

Here, two things can go horribly wrong:

The sets may not represent the entire population well. For instance, categories or numeric variables may be unevenly distributed between the train and test sets, leading to skewed learning.You risk leaking… Read the full blog for free on Medium.

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Published via Towards AI