Explainable AI (XAI) Methods Part 1 -- Partial Dependence Plot (PDP)


Explainable Machine Learning (XAI) refers to efforts to make sure that artificial intelligence programs are transparent in their purposes and how they work. This is understandable because a lot of SOTA (State of the Art) models are black boxes which are difficult to interpret or explain despite their top-notch predictive power and performance. For many organizations and corporations, several percentage increase in classification accuracy may not be as important as answers to questions like "how does feature A affect the outcome?" This is why XAI has been receiving more spotlight as it greatly aids decision making and performing causal inference. In the next series of posts, I will cover various XAI methodologies that are in wide use nowadays in the Data Science community.

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