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Local Explainability

Level of interpretation aiming to explain a model's prediction for a specific observation, isolating the impact of each feature for that particular case.

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Global Explainability

Level of interpretation that aggregates local explanations over a dataset to understand the general behavior of the model and the average importance of features.

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Base Value

In SHAP, the average prediction of the model on the training set, serving as the starting point to which feature contributions are added to obtain the final prediction.

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Additivity

Fundamental property of SHAP guaranteeing that the sum of SHAP values of all features, added to the base value, equals exactly the model's prediction for an observation.

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Efficiency

Axiom of Shapley value stating that the contributions of all features must add up to equal the difference between the model's prediction and the base value.

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Symmetry

Axiom of Shapley value ensuring that two features contributing identically to all possible coalitions receive the same importance value.

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Dummy

Axiom of Shapley value guaranteeing that a feature providing no marginal contribution to any coalition will be assigned a zero importance value.

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Interaction Value

Extension of SHAP values that quantifies the interaction effect between a pair of features, measuring how their combined impact differs from the sum of their individual impacts.

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