Binary classification is a common task for which machine learning and computational
statistics are used, and the area under the receiver operating characteristic curve (ROC …

To evaluate binary classifications and their confusion matrices, scientific researchers can
employ several statistical rates, accordingly to the goal of the experiment they are …

Evaluating binary classifications is a pivotal task in statistics and machine learning, because
it can influence decisions in multiple areas, including for example prognosis or therapies of …

As in many different areas of science and technology, most important problems in
bioinformatics rely on the proper development and assessment of binary classifiers. A …

To assess the quality of a binary classification, researchers often take advantage of a four-
entry contingency table called confusion matrix, containing true positives, true negatives …

Even if measuring the outcome of binary classifications is a pivotal task in machine learning
and statistics, no consensus has been reached yet about which statistical rate to employ to …

The receiver operating characteristic (ROC) has emerged as the gold standard for assessing
and comparing the performance of classifiers in a wide range of disciplines including the life …

Background The receiver operating characteristic (ROC) curve is a fundamental tool to
assess the discriminant performance for not only a single marker but also a score function …

Binary classifiers are routinely evaluated with performance measures such as sensitivity and
specificity, and performance is frequently illustrated with Receiver Operating Characteristics …

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 …