Abstract
Investigators discuss new findings in Machine Learning. According to news reporting out of Ottawa, Canada, by NewsRx editors, research stated, "There are multiple scenarios in machine learning where the data used presents a heavy bias towards one of the classes. Evaluating the performance of machine learning models in such imbalanced scenarios proves to be difficult and challenging, as one of the classes is poorly represented in the data, and this class is often more relevant to the end-user." Financial support for this research came from CGIAR. Our news journalists obtained a quote from the research from the University of Ottawa, "An abundance of performance metrics have been devised and commonly used in order to solve these specific problems, however, there is often a lack of common agreement on which metric is best and which to use in specific imbalanced scenarios. In this study, we experimentally study the impact of choosing one metric over another in the evaluation of a classifier for binary classification, as well as the effect of data characteristics such as class imbalance and noise on those metrics. Based on our extensive empirical analysis, we provide a set of easy-to-follow guidelines for which performance metric is best to use depending on the context of the problem."
NETL
NSTL
Springer Nature