mapie.metrics.classification.classification_mean_width_score

mapie.metrics.classification.classification_mean_width_score(y_pred_set: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → float

Mean width of prediction set output by _MapieClassifier.

Parameters

y_pred_set: NDArray of shape (n_samples, n_class, n_confidence_level)

Prediction sets given by booleans of labels.

Returns

NDArray of shape (n_confidence_level,)

Mean width of the prediction sets for each confidence level.

Examples

>>> import numpy as np
>>> from mapie.metrics.classification import classification_mean_width_score
>>> y_pred_set = np.array([
...     [[False, False], [False, True], [True, True]],
...     [[False, True], [True, False], [True, True]],
...     [[True, False], [True, True], [True, False]],
...     [[False, False], [True, True], [True, True]],
...     [[True, True], [False, True], [True, False]]
... ])
>>> print(classification_mean_width_score(y_pred_set))
[2.  1.8]

Examples using mapie.metrics.classification.classification_mean_width_score

LAC and APS methods explained

Set prediction example in the binary classification setting

Cross conformal classification explained