MCDMClassification Metric
Umahiri (Specificity)
Umahiri hupima uwiano wa visa halisi hasi ambavyo vilitambuliwa kwa usahihi kama hasi na kigeuzi. Inajibu swali: 'Kati ya visa vyote vilivyokuwa hasi kweli, ni vingapi tulikataa kwa usahihi?' Umahiri unakamilisha ukumbukaji na ni muhimu wakati chanya za uwongo zina gharama kubwa.
Soma mbinu kamili
Kwa wanachama pekee
IngiaIngia kwa akaunti ya bure ili kusoma sehemu hii.
Method map
The neighbourhood of related methods — select a node to explore.
Vyanzo
- Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI: 10.1016/j.patrec.2005.10.010 ↗
- Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63. link ↗
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 3). Specificity (True Negative Rate). ScholarGate. https://scholargate.app/sw/model-evaluation/specificity
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Usawa wa Usahihi (Balanced Accuracy)Tathmini ya Modeli↔ compare
- F1-ScoreTathmini ya Modeli↔ compare
- Kiwango cha Uwiano cha MatthewsTathmini ya Modeli↔ compare
- UsahihiTathmini ya Modeli↔ compare
- Kumbukumbu (Usikivu)Tathmini ya Modeli↔ compare
Imerejelewa na
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