model distillation for interpretability

**Model Distillation for Interpretability** is the **training of a simpler, interpretable model (student) to mimic the predictions of a complex, accurate model (teacher)** — transferring the complex model's knowledge into a form that humans can understand and verify. **Distillation for Interpretability** - **Teacher**: The accurate but opaque model (deep neural network, large ensemble). - **Student**: A simpler, interpretable model (linear model, small decision tree, GAM, rule list). - **Training**: The student is trained on the teacher's soft predictions (probabilities), not the original hard labels. - **Soft Labels**: The teacher's probability outputs contain "dark knowledge" about inter-class similarities. **Why It Matters** - **Best of Both Worlds**: Achieve near-complex-model accuracy with an interpretable model. - **Global Explanation**: The student model serves as a global explanation of the teacher's behavior. - **Deployment**: Deploy the interpretable student where transparency is required, backed by the teacher's validation. **Model Distillation** is **making the expert explain itself simply** — transferring a complex model's knowledge into an interpretable model for transparent decision-making.