===== Interpretability =====

'''Interpretability''' refers to the degree to which a human can understand the reasoning or internal mechanics of a model, algorithm, or system.

==== In machine learning ====
Interpretability means that:
  * The influence of each input variable on the model’s output can be understood.
  * Clinicians or researchers can trace how a decision or prediction was made.
  * The model’s behavior is transparent, explainable, and trustworthy.

==== Interpretability vs. Explainability ====
  * '''Interpretability''' often refers to simpler models (e.g., linear regression, decision trees) that are inherently transparent.
  * '''Explainability''' refers to methods (e.g., SHAP, LIME) that help clarify complex or black-box models like deep learning systems.

==== Clinical importance ====
High interpretability is critical when:
  * Decisions affect patient safety
  * Regulatory approval or ethical accountability is required
  * Clinicians must trust and verify model outputs

==== Limitations ====
  * More interpretable models may be less accurate.
  * Highly accurate models (e.g., neural networks) may lack interpretability, requiring post hoc explanation tools.

'''In summary:''' interpretability is essential for responsible use of data-driven tools in healthcare, as it connects model predictions to human understanding.