This workshop explains how to take the highest performing ML models such as gradient boosting and neural networks and understand what contributes to their predictions at a local and global level to make their output easily understood by practitioners and non-practitioners alike.

Description

Part 1: An Introduction to Interpretable ML

• Interpretability is why bad models are used more than they ought to be
• The current state of interpreting non-linear ML models, and their major shortcomings
• What's currently missing in the toolkit for understanding black box ML models

Part 2: An Introduction to SHAP

• A brief history of understanding black box models, and how it lead to the need for SHAP
• Why SHAP is a theoretically sound application of game theory to understand any ML model, regardless of how it generates predictions
• A close look at SHAP's source code to understand how it's used to compute its results

Part 3: SHAP in the Wild

• How to derive local explanations for a single model prediction (or how to be more like linear regression)
• Creating odds ratios
• Using SHAP to understand feature interaction effects among correlated data
• Using SHAP with neural networks and unstructured data: understanding word contributions to a Transformed NLP model
• Examples of SHAP being used in production

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