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 …

Regression analysis makes up a large part of supervised machine learning, and consists of
the prediction of a continuous independent target from a set of other predictor variables. The …

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 …

Breast cancer incidence has been rising steadily during the past few decades. It is the
second leading cause of death in women. If it is diagnosed early, there is a good possibility …

Deep Learning (DL) is currently transforming health services by significantly improving early
cancer diagnosis, drug discovery, protein–protein interaction analysis, and gene editing …

Even if assessing binary classifications is a common task in scientific research, no
consensus on a single statistic summarizing the confusion matrix has been reached so far …

Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of
children worldwide, and information about its prognosis can be pivotal for patients, their …

Medical imaging is a great asset for modern medicine, since it allows physicians to spatially
interrogate a disease site, resulting in precise intervention for diagnosis and treatment, and …

Accuracy assessment is essential in all image classification-related fields, ranging from
molecular imaging to earth observation. However, existing accuracy metrics are too …

Method. To investigate the usage of these three confusion matrix rates in robotics and
artificial intelligence, we performed a search of the Matthews correlation coefficient …