Related papers: Score-oriented loss (SOL) functions

Score-oriented loss (SOL) functions

URL: http://arxiv.org/abs/2103.15522v1
Date: Mon, 29 Mar 2021 11:45:53 GMT
Title: Score-oriented loss (SOL) functions
Authors: Francesco Marchetti and Sabrina Guastavino and Michele Piana and Cristina Campi
Abstract summary: This paper introduces a class of loss functions that are defined on probabilistic confusion matrices. The performances of these loss functions are validated during the training phase of two experimental forecasting problems.
Score: 1.433758865948252
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: 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.

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