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Obtaining Well Calibrated Probabilities Using Bayesian Binning
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in artificial intelligence. In this paper we present a new non-parametric calibration method called Bayesian Binning into Quantiles (BBQ) which addresses key limitations of exi...
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| Pubblicato in: | Proc AAAI Conf Artif Intell |
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| Autori principali: | , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2015
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4410090/ https://ncbi.nlm.nih.gov/pubmed/25927013 |
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