Interpretable Machine Learning

Some predictive machine learning models provide interpretable results, while others are black boxes. In either case, it’s important to know how and why a model made the predictions it did. This workshop will introduce tools for interpreting machine learning models and explaining their predictions. Topics covered include:

• Inherently interpretable models (linear and logistic regression, decision trees)
• Feature importance
• Individual conditional expectation (ICE) and partial dependence (PDP) plots
• Local surrogate models (LIME)
• Shapley additive explanations (SHAP)

All methods will be discussed at an approachable, non-technical level and demonstrated using worked examples in R and Python.

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