mapie.metrics
.spiegelhalter_statistic¶
- mapie.metrics.spiegelhalter_statistic(y_true: ndarray[Any, dtype[_ScalarType_co]], y_score: ndarray[Any, dtype[_ScalarType_co]]) float [source]¶
Compute Spiegelhalter’s statistic for calibration test. The closer to zero, the better the scores are calibrated. Indeed, if the scores are perfectly calibrated, the Brier score simplifies to an expression whose expectancy and variance are easy to compute. The statistic is no more that a z-score on this normalized expression.
- Parameters
- y_trueNDArray of shape (n_samples,)
An array of ground truth.
- y_scoreNDArray of shape (n_samples,)
An array of scores.
- Returns
- float
Spiegelhalter’s statistic.
References
Spiegelhalter DJ. Probabilistic prediction in patient management and clinical trials. Statistics in medicine. 1986 Sep;5(5):421-33.