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

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 …

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 …

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 …

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 …

Evaluating the performance of a learning algorithm is one of the basic tasks in machine
learning and data science. In this chapter, we review commonly used performance …

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 …

Abstract AUC (Area Under the Curve) of ROC (Receiver Operating Characteristics) has
been recently used as a measure for ranking performance of learning algorithms. In this …

The success of machine learning on a given task dependson, among other things, which
learning algorithm is selected and its associated hyperparameters. Selecting an appropriate …