Assessing feature importance with model-agnostic methods

Model-agnostic methods imply that we will not depend on intrinsic model parameters to compute feature importance. Instead, we will consider the model as a black box, with only the inputs and output visible. So, how can we determine which inputs made a difference?

What if we altered the inputs randomly? Indeed, one of the most effective methods for evaluating feature importance is through simulations designed to measure a feature’s impact or lack thereof. In other words, let’s remove a random player from the game and observe the outcome! In this section, we will discuss two ways to achieve this: permutation feature importance and SHAP.

Permutation feature importance

Once we have a trained model, we cannot remove a feature to assess the impact of not using it. However, we can:

• Replace the feature with a static value, such as the mean or median, rendering it devoid of useful information.
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