论文标题
洞悉机器学习的性能适应性和错误指标
Insights into Performance Fitness and Error Metrics for Machine Learning
论文作者
论文摘要
机器学习（ML）是训练机的领域，可实现高水平的认知并进行类似人类的分析。由于ML是一种数据驱动的方法，因此它似乎适合我们的日常生活和运营以及复杂和跨学科的领域。随着商业，开源和用户培养的ML工具的兴起，每当应用ML探索现象或场景时，通常就会出现一个关键问题：什么构成了好的ML模型？请记住，这个问题的适当答案取决于各种因素，这项工作假设一个好的ML模型是最佳性能并最能描述手头现象的模型。从这个角度来看，确定适当的评估指标以评估ML模型的性能，不仅是必要的，而且还需要。因此，本文研究了许多最常用的性能适应性和用于回归和分类算法的错误指标，重点是工程应用程序。
Machine learning (ML) is the field of training machines to achieve high level of cognition and perform human-like analysis. Since ML is a data-driven approach, it seemingly fits into our daily lives and operations as well as complex and interdisciplinary fields. With the rise of commercial, open-source and user-catered ML tools, a key question often arises whenever ML is applied to explore a phenomenon or a scenario: what constitutes a good ML model? Keeping in mind that a proper answer to this question depends on a variety of factors, this work presumes that a good ML model is one that optimally performs and best describes the phenomenon on hand. From this perspective, identifying proper assessment metrics to evaluate performance of ML models is not only necessary but is also warranted. As such, this paper examines a number of the most commonly-used performance fitness and error metrics for regression and classification algorithms, with emphasis on engineering applications.
