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Graphs showing how a model's prediction changes for an individual instance when a feature varies, revealing interactions and non-linear effects.

Examines the proximity of an instance to the model's decision boundary to quantify the degree of confidence and identify influential factors.

Combines decision rules learned by trees with linear effects to create a locally interpretable model while preserving performance.

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