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 …

We analyse 18 evaluation methods for learning algorithms and classifiers, and show how to
categorise these methods with the help of an evaluation method taxonomy based on several …

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 …

We analyze critically the use of classification accuracy to compare classifiers on natural data
sets, providing a thorough investigation using ROC analysis, standard machine learning …

ROC analysis investigates and employs the relationship between sensitivity and specificity
of a binary classifier. Sensitivity or true positiverate measures the proportion of positives …

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

Predictive accuracy has been used as the main and often only evaluation criterion for the
predictive performance of classification learning algorithms. In recent years, the area under …

This paper provides a characterization of bias for evaluation metrics in classi cation (eg,
Information Gain, Gini, 2, etc.). Our characterization provides a uniform representation for all …

Abstract Evaluation measures play an important role in machine learning because they are
used not only to compare different learning algorithms, but also often as goals to optimize in …

This essay gives advice to authors of papers on machine learning, although much of it
carries over to other computational disciplines. The issues covered include the material that …