… learning studies and the respective evaluation metrics that … The various evaluation metrics
were used to evaluate the … common metrics in practice used by many researchers to evaluate …

… dynamic, trainable evaluation metrics for machine translation. … metric families are special
cases of BLANC, and we compare correlations with human judgments across these three metric …

… In this paper we analyze the most popular evaluation metrics for separate-and-conquer rule
learning algorithms. Our results show that all commonly used heuristics, including accuracy, …

… Explainability is used in the remainder of this paper and the taxonomy of evaluation metrics
is … This paper reviews evaluation metrics of ML explanations based on these three types of …

… The purpose of this chapter is to survey existing evaluation metrics and discuss … metrics
that were not specifically designed to handle class imbalances as well as more recent metrics …

… evaluate the performance of ML models and compare them with each other. Here, we introduce
the most common evaluation metrics … obtain enough values of the metric for testing, and …

… proposed metrics with existing metrics for learning disentangled representations. For deeper
analysis of these metrics… One can easily see that only our metrics satisfy the aforementioned …

… Here we directly learn a differentiable mapping to evaluation metrics, bypassing the tedious
… We consider optimizing the evaluation metric M of a neural network model in a finetuning …

… Thus, we present a brief overview of ML, explain the metrics used in … metrics of machine
learning, can help those with little or no technical knowledge begin to assess machine learning …

… The result is a model for evaluating the reusability of learning objects, … evaluating the
reusability of learning objects are analyzed. In Sect. 3, a study is made of those features of learning …