Model Interpretability & Explainability — Machine Learning Roadmap
Understanding and explaining why a model makes the predictions it does
Steps in Model Interpretability & Explainability
- Feature Importance — advanced · Built-in importance scores from tree-based models and their limitations
- SHAP Values — advanced · A game-theory-based approach to explaining individual predictions consistently
- LIME — advanced · Explaining individual predictions by locally approximating the model with a simpler one
- Partial Dependence Plots — advanced · Visualizing how a feature affects predictions on average, holding others constant
- Communicating Model Decisions to Stakeholders — advanced · Translating technical explanations into something a non-technical decision-maker can act on
Part of
- Machine Learning roadmap — the full learning path