Machine Learning Made Simple: Permutation Based Feature Importance

Slides: https://www.slideshare.net/Devansh16/machine-learning-made-simple-permutation-based-variable-importance

In this iteration of #MachineLearningMadeSimple we are going to look at Permutation Based Feature Importance. This is a very convenient way to calculate how important a variable to feature is important to your model.
One of the most basic questions we might ask of a model is: What features have the biggest impact on predictions?

This concept is called feature importance.

"There are multiple ways to measure feature importance. Some approaches answer subtly different versions of the question above. Other approaches have documented shortcomings.

In this lesson, we'll focus on permutation importance. Compared to most other approaches, permutation importance is:

1) fast to calculate,
2) widely used and understood, and
3) consistent with properties we would want a feature importance measure to have.

Read more here: https://christophm.github.io/interpretable-ml-book/feature-importance.html

Python Sklearn: https://scikit-learn.org/stable/modules/permutation_importance.html

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