The selection of the best classification algorithm for a given dataset is a very widespread
problem. It is also a complex one, in the sense it requires to make several important …

We analyze critically the use of classification accuracy to compare classifiers on natural data
sets, providing a thorough investigation using ROC analysis, standard machine learning …

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 …

This paper promotes a new task for supervised machine learning research: quantification-
the pursuit of learning methods for accurately estimating the class distribution of a test set …

Machine learning (ML) is the field of training machines to achieve high level of cognition and
perform human-like analysis. Since ML is a data-driven approach, it seemingly fits into our …

In this article we analyze the effect of class distribution on classifier learning. We begin by
describing the different ways in which class distribution affects learning and how it affects the …

Many subfields of machine learning share a common stumbling block: evaluation. Advances
in machine learning often evaporate under closer scrutiny or turn out to be less widely …

Dataset scaling, also known as normalization, is an essential preprocessing step in a
machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary …

We present a statistical analysis of the AUC as an evaluation criterion for classification
scoring models. First, we consider significance tests for the difference between AUC scores …

This paper provides both an introduction to and a detailed overview of the principles and
practice of classifier calibration. A well-calibrated classifier correctly quantifies the level of …