Short Definition
Model interpretability explains why a model makes predictions.
Definition
Model interpretability focuses on understanding how inputs influence outputs. Rather than treating models as black boxes, interpretability techniques aim to reveal feature importance, decision paths, or sensitivity.
This is critical in high-stakes or regulated applications.
Why It Matters
Interpretable models build trust and enable debugging.
How It Works (Conceptually)
- Analyze weights and activations
- Attribute importance to inputs
- Examine decision behavior
Minimal Python Example
importance = abs(weight)Common Pitfalls
- Treating explanations as exact truths
- Ignoring uncertainty
- Applying interpretability only after failure
Related Concepts
- Feature Attribution
- Sparse Networks
- Debugging Models
Neural Network Lexicon 