Cracking the Box: Interpreting Black Box Machine Learning Models

To kick off this article, I’d like to explain the interpretability of a machine learning (ML) model.

According to Merriam-Webster, interpretability describes the process of making something plain or understandable. In the context of ML, interpretability provides us with an understandable explanation of how a model behaves. Basically, it helps us figure out what’s behind model predictions and how these models work. Miller and Tim’s “Explanation in Artificial Intelligence: Insights from the Social Sciences” states that…

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