Accumulated Local Effect Plots (ALEs) | Explanation & Python Code
Highly correlated features can wreak havoc on your machine-learning model interpretations. To overcome this, we could rely on good feature selection. But there are still cases when a feature, although highly correlated, will provide some unique information leading to a more accurate model. So we need a method that can provide clear interpretations, even with multicollinearity. Thankfully we can rely on ALEs.
We give you the intuition for how ALEs are created, formally define the algorithm used to create ALEs and apply ALEs using Python and the Alibi Explain package. We will see that, unlike other XAI methods like SHAP, LIME, ICE Plots and Friedman's H-stat, ALEs give interpretations that are robust to multicollinearity.
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🚀 Chapters 🚀
00:00 Introduction
01:17 Intuition
04:39 Formal Algorithm
07:22 Python Code
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