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Metric

AUCalibration

API referenceSource
Multivariate

Area under the calibration curve for distributional predictions.

Quickstart

python
from sktime.performance_metrics.forecasting.probabilistic import AUCalibration

estimator = AUCalibration(multioutput='uniform_average', multivariate=False)

Parameters(2)

multioutput‘uniform_average’ (default), 1D array-like, or ‘raw_values’

Whether and how to aggregate metric for multivariate (multioutput) data.

  • If 'uniform_average' (default), errors of all outputs are averaged with uniform weight.

  • If 1D array-like, errors are averaged across variables, with values used as averaging weights (same order).

  • If 'raw_values', does not average across variables (outputs), per-variable errors are returned.

multivariatebool, optional, default=False

  • if True, behaves as multivariate metric (sum of scores): the metric is computed for entire row, results one score per row

  • if False, is univariate metric, per variable: the metric is computed per variable marginal, results in many scores per row