pysteps.verification.detcatscores.det_cat_fct_compute¶
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pysteps.verification.detcatscores.
det_cat_fct_compute
(contab, scores='')¶ Compute simple and skill scores for deterministic categorical (dichotomous) forecasts from a contingency table object.
Parameters: - contab : dict
A contingency table object initialized with pysteps.verification.detcatscores.det_cat_fct_init and populated with pysteps.verification.detcatscores.det_cat_fct_accum.
- scores : {string, list of strings}, optional
The name(s) of the scores. The default, scores=”“, will compute all available scores. The available score names a
Name Description ACC accuracy (proportion correct) BIAS frequency bias CSI critical success index (threat score) F1 the harmonic mean of precision and sensitivity FA false alarm rate (prob. of false detection, fall-out, false positive rate) FAR false alarm ratio (false discovery rate) GSS Gilbert skill score (equitable threat score) HK Hanssen-Kuipers discriminant (Pierce skill score) HSS Heidke skill score MCC Matthews correlation coefficient POD probability of detection (hit rate, sensitivity, recall, true positive rate) SEDI symmetric extremal dependency index
Returns: - result : dict
Dictionary containing the verification results.