Which method is used to explain model predictions while keeping interpretations local and interpretable?

The method that is designed specifically to explain model predictions in a local and interpretable manner is Local Interpretable Model-agnostic Explanations, commonly known as LIME. LIME operates by approximating the complex model in the vicinity of the prediction being explained with a simpler, interpretable model. This allows users to understand how individual features contribute to a specific prediction rather than providing an overall interpretation of the entire model.

LIME achieves this by perturbing the input data and observing how the predictions change, which effectively emphasizes the local behavior of the model in relation to that specific prediction. This technique is particularly valuable since it addresses the need for transparency in complex models, making it easier for users to trust and understand the outcomes provided by machine learning algorithms.

Other methods mentioned, while valuable in their contexts, do not prioritize local interpretations as much as LIME does. Regression Analysis Techniques often provide global trends rather than insights into specific predictions. Comparative Algorithm Assessment generally focuses on evaluating different modeling approaches without delving into individual predictions. Model Complexity Review typically aims at assessing the intricacy of the model itself rather than explaining its predictions. Thus, LIME stands out as the most suitable choice for local interpretability in model predictions.