SHAP
Artificial Intelligence
Demystifying Bayesian Models: Unveiling Explanability through SHAP Values The Gap between Bayesian Models and Explainability Bayesian modelization with PyMC Explain the model with SHAP Conclusion
Exploring PyMC’s Insights with SHAP Framework via an Engaging Toy ExampleSHAP values (SHapley Additive exPlanations) are a game-theory-based method used to extend the transparency and interpretability of machine learning models. Nevertheless, this method, together...
Artificial Intelligence
Machine Learning, Illustrated: Opening Black Box Models with SHAP
Now that we understand the underlying calculations of SHAP, we are able to apply it to our predictions by visualizing them. To visualise them, we'll use from Python’s library and input our...
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