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Metric

LogLoss

API referenceSource
Multivariate

Logarithmic loss for distributional predictions.

For a predictive distribution \(d\) with pdf \(p_d\) and a ground truth value \(y\), the logarithmic loss is defined as \(L(y, d):= -\log p_d(y)\).

  • evaluate computes the average test sample loss.

  • evaluate_by_index produces the loss sample by test data point.

  • multivariate controls averaging over variables.

Quickstart

python
from sktime.performance_metrics.forecasting.probabilistic import LogLoss

estimator = LogLoss(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 log-loss: the log-loss is computed for entire row, results one score per row

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