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Interpretability in the context of Machine Learning\n\t2. Local vs Global Interpretability\n\t3. Intrinsically explainable models\n\t4. Post-hoc explainability methods\n\t5. Challenges to interpretability\n\t6. How to make models more explainable\n\n2. **Intrinsically Explainable Models**\n\t1. Linear and Logistic Regression \n\t2. Decision trees\n\t3. Random forests\n\t4. Gradient boosting machines\n\t5. Global and local interpretation\n\n3. **Post-hoc methods - Global explainability**\n\t1. Permutation Feature Importance\n\t2. Partial dependency plots\n\t3. Accumulated local effects\n\n4. **Post-hoc methods - Local explainability**\n\t1. LIME\n\t2. SHAP\n\t3. Individual contitional expectation\n\n5. **Featuring the following Python interpretability libraries**\n   \t1. Scikit-learn\n   \t2. treeinterpreter\n   \t3. Eli5\n   \t4. Dalex\n   \t5. Alibi\n   \t6. pdpbox\n   \t7. Lime\n   \t8. Shap\n\n## Links\n\n- [Online Course](https://www.trainindata.com/p/machine-learning-interpretability)\n","funding_links":["https://github.com/sponsors/solegalli"],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsolegalli%2Fmachine-learning-interpretability","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsolegalli%2Fmachine-learning-interpretability","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsolegalli%2Fmachine-learning-interpretability/lists"}