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 …

The evaluation of classifiers or learning algorithms is not a topic that has, generally, been
given much thought in the fields of Machine Learning and Data Mining. More often than not …

In this note we use examples from the literature to illustrate some poor practices in
assessing the performance of supervised classification rules, and we suggest guidelines for …

Performance metrics in classification are fundamental in assessing the quality of learning
methods and learned models. However, many different measures have been defined in the …

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

Common arguments for including applications papers in the Machine Learning literature are
often based on the papers' value for advertising success stories and for morale boosting. For …

Different evaluation measures assess different characteristics of machine learning
algorithms. The empirical evaluation of algorithms and classifiers is a matter of on-going …

Cross-validation is a mainstay for measuring performance and progress in machine
learning. There are subtle differences in how exactly to compute accuracy, F-measure and …

The proportion of successful hits, usually referred to as “accuracy”, is by far the most
dominant meter for measuring classifiers' accuracy. This is despite of the fact that accuracy …

Abstract Machine learning models have been widely used in numerous classification
problems and performance measures play a critical role in machine learning model …