Comparing model performances on benchmark datasets is an integral part of measuring
and driving progress in artificial intelligence. A model's performance on a benchmark …

Developing state-of-the-art approaches for specific tasks is a major driving force in our
research community. Depending on the prestige of the task, publishing it can come along …

Strong empirical evidence that one machine-learning algorithm A outperforms another one
B, ideally calls for multiple trials optimizing the learning pipeline over sources of variation …

Performance metrics are pivotal in machine learning field, especially for tasks like
regression, classification, and clustering (Saura, 2021). They offer quantitative measures to …

This paper gives an overview of some ways in which our understanding of performance
evaluation measures for machine-learned classifiers has improved over the last twenty …

Benchmarks are crucial to measuring and steering progress in artificial intelligence (AI).
However, recent studies raised concerns over the state of AI benchmarking, reporting issues …

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 …

Abstract Model selection strategies for machine learning algorithms typically involve the
numerical optimisation of an appropriate model selection criterion, often based on an …

Machine learning research depends on objectively interpretable, comparable, and
reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites …

Background Assessing the performance of machine learning (ML) models requires careful
consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics …