Absolute Evaluation Measures for Machine Learning: A Survey
Journal:
arXiv
Published Date:
Jul 4, 2025
Abstract
Machine Learning is a diverse field applied across various domains such as
computer science, social sciences, medicine, chemistry, and finance. This
diversity results in varied evaluation approaches, making it difficult to
compare models effectively. Absolute evaluation measures offer a practical
solution by assessing a model's performance on a fixed scale, independent of
reference models and data ranges, enabling explicit comparisons. However, many
commonly used measures are not universally applicable, leading to a lack of
comprehensive guidance on their appropriate use. This survey addresses this gap
by providing an overview of absolute evaluation metrics in ML, organized by the
type of learning problem. While classification metrics have been extensively
studied, this work also covers clustering, regression, and ranking metrics. By
grouping these measures according to the specific ML challenges they address,
this survey aims to equip practitioners with the tools necessary to select
appropriate metrics for their models. The provided overview thus improves
individual model evaluation and facilitates meaningful comparisons across
different models and applications.
