holisticai.explainability.metrics.alpha_score

holisticai.explainability.metrics.alpha_score(feature_importance: ArrayLike, alpha: float = 0.8)

Alpha Score calculates the smallest proportion of features that account for the alpha percentage of the overall feature importance.

Parameters

feature_importance: Importances

The feature importance values.

alpha: float, optional

The alpha value represents the percentage of importance that will be considered in the calculation. For example, if alpha=0.8, the top 80% of the most important features will be considered.

Returns

float

The alpha importance score

Examples

>>> from holisticai.explainability.commons import Importance
>>> from holisticai.explainability.metrics import alpha_score
>>> values = np.array([0.50, 0.30, 0.20])
>>> feature_names = ["feature_1", "feature_2", "feature_3"]
>>> feature_importance = Importance(values=values, feature_names=feature_names)
>>> alpha_score(feature_importance)
0.6666666666666666