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 …

This thesis investigates some fundamental issues of performance measures of machine
learning. Performance measures (or evaluation measures) play important roles in machine …

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 …

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 …

In order to rank the performance of machine learning algorithms, many researchers conduct
experiments on benchmark data sets. Since most learning algorithms have domain-specific …

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 …

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 …

The fundamental question studied in this thesis is how to evaluate and analyse supervised
learning algorithms and classifiers. As a first step, we analyse current evaluation methods …

Most evaluation metrics in classification are designed to reward class uniformity in the
example subsets induced by a feature (eg, Information Gain). Other metrics are designed to …