Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm’s performance. We show that a deep

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Abstract--In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi

In this paper we propose methods to detect and repair concavities in ROC curves by manipulating model predictions. We introduce two model assembly algorithms. Algorithm SwapOne aims to improve the Area Under the ROC Curve (AUC) of a probabilistic classifier by investigating three models from

Resampling procedures allows a better use of ROC curves and AUC for predictive purposes. We also address a drawback of AUC for the comparison of ROC curves which are crossing, by recommending the use of partial AUC. 1 ROC curve and AUC as a measure of performance Receiver operating characteristic

Abstract: The receiver operating characteristic (ROC) curve is used to describe the performance of a diagnostic test which classifies observations into two groups. We introduce a new method for selecting bandwidths when computing kernel estimates of ROC curves. Our technique allows for interaction

This paper is devoted to thoroughly investigating how to bootstrap the ROC curve, a widely used visual tool for evaluating the accuracy of test/scoring statistics in the bipartite setup. The issue of confidence bands for the ROC curve is considered and a resampling procedure based on a smooth

In this paper we study techniques for generating and evaluat-ing confidence bands on ROC curves. ROC curve evaluation is rapidly becoming a commonly used evaluation metric in machine learning, although evaluating ROC curves has thus far been lim-ited to studying the area under the curve (AUC

, and negative, otherwise. Therefore, false positive and false negative classifications can be generated. The performance of test can be evaluated by ROC curves which defined, for a given threshold, the compro-mise between Sensitivity and Specificity, i.e., the True and False Positive fractions. In this work, we

• This work overviews some developments on the estimation of the Receiver Operating Characteristic (ROC) curve. Estimation methods in this area are constantly being developed, adjusted and extended, and it is thus impossible to cover all topics and areas of application in a single paper. Here, we