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--- ai_explainability_manager [2025/05/27 01:17] – [Best Practices] eagleeyenebula
+++ ai_explainability_manager [2025/05/27 01:24] (current) – [Class Overview] eagleeyenebula
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 ====== AI Explainability Manager ======
 **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:
-The **AI Explainability Manager System** leverages **SHAP** (SHapley Additive exPlanations) to provide detailed insights into machine learning model predictions. By calculating and visualizing **SHAP values**, this system enables practitioners to understand the contribution of each input feature to the prediction outcome, enhancing model transparency and aiding in debugging or stakeholder trust.
+The **AI Explainability Manager System** leverages **SHAP** (**SHapley Additive exPlanations**) to provide detailed insights into machine learning model predictions. By calculating and visualizing **SHAP values**, this system enables practitioners to understand the contribution of each input feature to the prediction outcome, enhancing model transparency and aiding in debugging or stakeholder trust.
 {{youtube>1ENdR5Qq4Q4?large}}
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 * **Outputs**:
-  * A **SHAP** summary plot visualizing the feature importance for the given `input_data`.
+  * A **SHAP** summary plot visualizing the feature importance for the given **input_data**.
 ===== Usage Examples =====
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 </code>
 **Explanation**:
-* The **ExplainabilityManager** uses the trained Random Forest model and a representative sample of training data (`X`) to calculate SHAP values.
+  * The **ExplainabilityManager** uses the trained Random Forest model and a representative sample of training data (`**X**`) to calculate **SHAP** values.
-* It visualizes a **SHAP** **summary plot**, showing how each feature contributes to the prediction for **input_data**.
+  * It visualizes a **SHAP** **summary plot**, showing how each feature contributes to the prediction for **input_data**.
 ==== Example 2: Explaining Multiple Predictions ====