Machine learning methods are now an important tool for scientists, researchers, engineers,
and students in a wide range of areas. Many years ago, one could publish papers …

In an article in this issue, Webb and Ting criticize ROC analysis for its inability to handle
certain changes in class distributions. They imply that the ability of ROC graphs to depict …

Abstract Machine learning is a powerful tool for gleaning knowledge from massive amounts
of data. While a great deal of machine learning research has focused on improving the …

In this paper we show an efficient method for inducing classifiers that directly optimize the
area under the ROC curve. Recently, AUC gained importance in the classification …

Abstract Machine learning research is often conducted in vitro, divorced from motivating
practical applications. A researcher might develop a new method for the general task of …

Many performance metrics have been introduced in the literature for the evaluation of
classification performance, each of them with different origins and areas of application …

A variety of different performance metrics are commonly used in the machine learning
literature for the evaluation of classification systems. Some of the most common ones for …

Precision-recall (PR) curves and the areas under them are widely used to summarize
machine learning results, especially for data sets exhibiting class skew. They are often used …

In Machine Learning, performance measurement is an essential task. So when it comes to a
classification problem, we can count on an AUC-ROC Curve. When we need to check or …

Null hypothesis significance testing is routinely used for comparing the performance of
machine learning algorithms. Here, we provide a detailed account of the major underrated …