Black-box Models

Black-box models refer to machine learning models that are complex and difficult to interpret. These models are often used in situations where the accuracy of the model is more important than understanding how the model works. Black-box models are often used in areas such as image recognition, natural language processing, and speech recognition. The lack of interpretability of black-box models can be a disadvantage in some situations, such as when the model is used in a regulated industry where transparency is required. However, there are techniques such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) that can be used to provide some level of interpretability to black-box models.
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