Accumulated Local Effects (ALE) is a statistical technique used primarily in the context of interpreting machine learning models, particularly those that are complex and difficult to understand, such as ensemble methods or neural networks. ALE provides insights into how the predicted outcomes of a model change as individual features (or variables) are varied.
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