Audience

This talk is for Data Scientists and Machine Learning Engineers at any level. Basic knowledge of machine learning is useful but not necessary.

Objective

Attendees will learn why explainable machine learning is important and how to use and interpret SHAP values for their model.

Details

ML models behave as black boxes in most scenarios. The model predicts or provides a certain output, but it is very difficult to generate any actionable insights directly. This is mostly because we generally have no idea which features are contributing the most to the model's behavior internally. SHAP provides a way to explain model predictions and can be an important tool in a data scientist's toolbox.

In this talk, we will begin by explaining to the audience the need for explainability and why it is essential to understand beyond what the model outputs. We will then briefly review the mathematical intuition behind Shapley values and their origins in game theory. After that, we will walk through a couple of case studies of tree-based and neural network-based models. We will be focusing on the interpretation of SHAP through various plots. Finally, we will discuss the best practices for interpreting SHAP visualizations, handling large datasets, and common pitfalls to avoid.

Outline

• Introduction and motivation [1 min]
• Why explainability matters? [5 min]
• Problem with black box models
• Actionable insights
• SHAP theory and intuition [5 min]
  • Shapley values
  • Game theory origins
  • SHAP
• Case study 1: Tree-based model [4 min]
  • Problem definition
  • model output
  • SHAP visualization
  • Global plots
  • Local plots
  • Interpretation
• Case study 2: Neural Network model [8 min]
  • Problem definition
  • Model output
  • SHAP visualization
  • Global plots
  • Local plots
  • Interpretation
• Best practices and common pitfalls [4 min]
  • Interpret SHAP correctly
  • Avoid misleading explanations
  • Performance challenges for large datasets
  • Other techniques for explainability
• Q/A [3 min]