Phase 3: Explainability & Transparency · 50 min · Python · SHAP · scikit-learn

SHAP — From Global to Local Explanations

Every prediction has a story. SHAP tells it with math.

Hiring signal: SHAP is the most commonly required explainability tool in RAI job postings. Being able to generate global summary plots and local waterfall/force plots — and interpret them for stakeholders — is a core technical competency for the role.

What you will learn

Explain Shapley values and their origin in cooperative game theory
Use TreeExplainer for tree-based models and KernelExplainer for model-agnostic explanations
Generate SHAP global summary plots and local waterfall/force plots
Interpret SHAP values for technical and non-technical stakeholders

The Problem

You have a trained model. It's accurate. It's also a black box. You need to answer two questions:

Global: Which features matter most across all predictions?
Local: Why was this specific person denied a loan?

SHAP (SHapley Additive exPlanations) answers both. It's based on Shapley values from cooperative game theory — a concept Lloyd Shapley introduced in 1953 for fairly distributing payouts among coalition players. In ML, the "players" are features, the "game" is the model's prediction, and the "payout" is the difference between the prediction and the baseline.

How SHAP Works

For a model with features $x_1, x_2, ..., x_n$ producing prediction $f(x)$:

$$f(x) = \phi_0 + \sum_{i=1}^{n} \phi_i$$

Where $\phi_0$ is the baseline (average prediction) and $\phi_i$ is the SHAP value for feature $i$ — its contribution to moving the prediction away from the baseline.

Key properties:

Efficiency: SHAP values sum to the difference between the prediction and the baseline
Symmetry: Features with equal impact get equal SHAP values
Additivity: SHAP values from ensemble models decompose into per-model contributions

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers TreeExplainer vs. KernelExplainer, Building the SHAP Explanation Module, What's Next — plus a hands-on lab, quiz, and project artifact.

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