Model Explainability is The Next Data Science Superpower
This is interesting:<p>A Kaggle competition to predict loan defaults gives an extreme example. This competition had 100s of raw features. For privacy reasons, the features had names like f1, f2, f3 rather than common English names. This simulated a scenario where you have little intuition about the raw data.<p>One competitor found that the difference between two of the features, specifically f527 — f528, created a very powerful new feature. Models including that difference as a feature were far better than models without it. But how might you think of creating this variable when you start with hundreds of variables?<p>The techniques you’ll learn in [OP's cours] would make it transparent that f527 and f528 are important features, and that their role is tightly entangled. This will direct you to consider transformations of these two variables, and likely find the “golden feature” of f527 — f528.