In this video, we delve into the fascinating world of black box machine learning models and how to interpret them using the SHAP (SHapley Additive exPlanations) Python library. Black box models, such as deep neural networks and gradient boosting, are known for their exceptional predictive power. Still, understanding the reasons behind their predictions can be challenging. SHAP comes to the rescue by providing us with insightful explanations for these complex models.
SHAP library documentation: https://shap.readthedocs.io/en/latest/
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1. Introduction: 0:02
2. SHAP Library overview: 0:23
3. Install SHAP and import: 1:00
4. Coding and Understanding: 1:45
5. Waterfall chart: 3:16
6. Force Chart: 4:28
7. Beeswamp Chart: 5:39
8. Conclusion: 6:22
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