🔍 Explainable Predictions

Evaluation metrics like accuracy, precision, and recall don't tell the full story. Stakeholders need to understand how and why models make predictions for debugging, regulatory compliance, building user trust, and making business decisions.

🛠️ Explainability Approaches

Explainability methods help answer "Why did the model predict X?" Most methods work by assigning attribution values to features—numbers showing how much each feature contributed to the prediction. This section covers the foundational concepts and five major explainability approaches.

What You'll Learn:

1. Model Baselines - The reference point that makes attribution values meaningful
2. SHAP - Framework-agnostic method based on game theory
3. Integrated Gradients (IG) - Gradient-based method for neural networks
4. Sampled Shapley - Cloud-deployable variant of SHAP
5. Counterfactual Analysis - Finding "what if" scenarios
6. Example-Based Explanations - Showing similar training examples

🧵 Attribution Values: The Common Thread

All these techniques produce attribution values—numerical indicators of feature importance:

• Positive values: Feature pushed prediction up
• Negative values: Feature pushed prediction down
• Magnitude: Strength of influence

But to interpret "cholesterol = +0.4" or "blood pressure = -0.2," you need to know: relative to what? That's where baselines come in.