Interpreting Machine Learning Models (Decoding the Black Box)

Interpreting Machine Learning models is no longer a luxury but a necessity. In this session, we will explore practical techniques to interpret ML models using real time datasets across domains. Explainable AI is a developing field and many of the ideas presented here are pretty new.

Below are the broad topics to be covered :

•Feature Importances

•Partial Dependence Plots

•ICE Plots

•Model Prediction Explanations with LIME

•Building Interpretable Models with Surrogate Tree- based Models

•Model Prediction Explanation with SHAP values

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