机器学习回归、预测和预后中的性能指标（误差度量）：性质和类型学
Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology
摘要：
业绩计量（误差计量）是各个领域评价框架的重要组成部分。本研究的目的是概述各种性能指标和方法，他们的分类。这项研究的主要目标是开发一种类型学，这将有助于提高我们对指标的知识和理解，并促进它们在机器学习回归、预测和预测中的选择。在分析众多性能指标结构的基础上，我们提出了一个指标框架，它包括四（4）类：主要指标，扩展指标，复合指标，和混合集的指标。本文确定了决定主要指标结构和属性的三（3）个关键组件（维度）：确定点距离的方法、归一化方法、数据集上点距离的聚合方法。
英文摘要：
Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. The intention of this study was to overview of a variety of performance metrics and approaches to their classification. The main goal of the study was to develop a typology that will help to improve our knowledge and understanding of metrics and facilitate their selection in machine learning regression, forecasting and prognostics. Based on the analysis of the structure of numerous performance metrics, we propose a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set.
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。
