  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.
