Score-oriented loss (SOL) functions
Università di Padova（帕多瓦大学）；Università di Genova（热那亚大学）；CNR - SPIN Genova（意大利国家研究委员会-热那亚超导、创新材料与器件研究所）
英文摘要：
Loss functions engineering and the assessment of forecasting performances are two crucial and intertwined aspects of supervised machine learning. This paper focuses on binary classification to introduce a class of loss functions that are defined on probabilistic confusion matrices and that allow an automatic and a priori maximization of the skill scores. The performances of these loss functions are validated during the training phase of two experimental forecasting problems, thus showing that the probability distribution function associated with the confusion matrices significantly impacts the outcome of the score maximization process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
