Insights into Performance Fitness and Error Metrics for Machine Learning

TL;DR

This paper reviews key performance and error metrics used to evaluate machine learning models, emphasizing their importance in assessing model quality in engineering applications.

Contribution

It provides a comprehensive analysis of commonly-used metrics for regression and classification, highlighting their roles in determining model performance.

Findings

Identifies essential metrics for evaluating ML models.

Highlights the importance of metric selection based on application.

Provides guidance for assessing model fitness in engineering contexts.

Abstract

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.