Update 4-explainability-vs-interpretability.md

episodes/4-explainability-vs-interpretability.md

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-title: "Explainability versus interpretability"
+title: "Interpretablility versus explainability"
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+- What are popular machine learning models? 
+- What are model intepretability and model explainability? Why are they important?
+- Which should you choose: interpretable models or explainable models?
 
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+- Showcase machine learning models that are widely used in practice.
+- Understand and distinguish between explainable machine learning models and interpretable machine learning models.
+- Describe two reasons when deciding which model to choose.
 
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+- **Model Explainability vs. Model Interpretability:**
+   - **Interpretability:** Refers to the degree to which a human can understand the cause of a decision made by a model. It is essential for verifying the correctness of the model, ensuring compliance with regulations, and enabling effective debugging.
+   - **Explainability:** Refers to the extent to which the internal mechanics of a machine learning model can be explained in human terms. It is crucial for understanding how models make decisions, ensuring transparency, and building trust with stakeholders.
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+- **Choosing Between Explainable and Interpretable Models:**
+   - **When Transparency is Critical:** Opt for interpretable models (e.g., linear regression, decision trees) when it is essential to have a clear understanding of how decisions are made, such as in healthcare or finance.
+   - **When Performance is a Priority:** Choose explainable models (e.g., neural networks, gradient boosting machines) when predictive accuracy is the primary concern, and you can use explanation methods to understand model behavior.
 
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