Model-Agnostic Methods

Model-agnostic methods are those that can be applied to any machine learning model, regardless of the algorithm used. These methods aim to provide insights into how the model arrives at its predictions or decisions.

LIME (Local Interpretable Model-Agnostic Explanations)

One such method is LIME, which generates explanations for individual predictions by approximating the model's behavior in a local neighborhood around the prediction. For example, LIME can be used to explain why a certain image was classified as a dog by a convolutional neural network (CNN). LIME generates an explanation by selecting a small set of pixels in the image that were most influential in the CNN's decision.

SHAP (SHapley Additive exPlanations)

Another model-agnostic method is SHAP, which provides a unified framework for interpreting the output of any machine learning model as a combination of feature attributions. In other words, SHAP explains the contribution of each feature to the model's prediction. For example, SHAP can be used to explain why a certain customer was targeted with a specific advertisement by a recommendation system. SHAP generates an explanation by attributing a score to each feature based on its impact on the recommendation.

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