===== SHAP (Shapley Additive Explanations) =====

'''SHAP''' stands for '''Shapley Additive Explanations'''. It is a method from cooperative game theory applied to machine learning to explain how much each input feature contributes to a model’s output.

Originally derived from Shapley values in game theory, SHAP calculates the average **marginal contribution** of each feature to the prediction, across all possible combinations of features.

==== Key characteristics ====
  * **Model-agnostic**: Can be applied to any machine learning model.
  * **Additive**: The sum of SHAP values equals the model output.
  * **Interpretable**: Assigns an importance score to each variable for a specific prediction.

==== Clinical relevance ====
SHAP is increasingly used in medical AI to:
  * Understand which variables drive risk predictions.
  * Improve transparency in black-box models.
  * Support clinician trust in algorithmic decision tools.

==== Limitations ====
  * SHAP explains the behavior of the **model**, not the **underlying physiology**.
  * It does **not imply causality** — a high SHAP value does not mean a variable causes disease.
  * Computationally expensive for complex models and large datasets.

'''In summary:''' SHAP helps interpret machine learning outputs, but must be used with caution in clinical settings to avoid overinterpreting spurious correlations.