Explainability
Explainability refers to the ability of a machine learning model to provide clear and understandable explanations for its predictions or decisions. It is an important aspect of artificial intelligence and data science, especially in applications where the decisions made by the model have significant impact on human lives, such as healthcare, finance, and criminal justice. Explainability can be achieved through various techniques such as feature importance analysis, decision tree visualization, and local surrogate models. The goal of explainability is to increase transparency, accountability, and trust in machine learning models, and to enable humans to understand and validate the reasoning behind the model's predictions or decisions.
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