[SciPy-User] ANN: properscoring: proper scoring rules in Python

[Stephan Hoyer]

I'm pleased to announce the release of a new open source package,
properscoring, for calculating proper scoring rules in Python:
https://github.com/TheClimateCorporation/properscoring

Evaluation methods that are "strictly proper" cannot be artificially
improved through hedging, which makes them fair methods for accessing the
accuracy of probabilistic forecasts. These methods are useful for
evaluating machine learning or statistical models that produce
probabilities instead of point estimates. In particular, these rules are
often used for evaluating weather forecasts.

properscoring currently contains optimized and extensively tested routines
for calculating the Continuous Ranked Probability Score (CRPS) and the
Brier Score:
- CRPS for an ensemble forecast
- CRPS for a Gaussian distribution
- CRPS for an arbitrary cumulative distribution function
- Brier score for binary probability forecasts
- Brier score for threshold exceedances with an ensemble forecast

If you're interested in these types of metrics, we'd love to hear your
thoughts on this package.

Cheers,
Stephan
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