1 AUC Estimation The area under the ROC curve can be approximated using lower rectangles, upper rectangles or by using a linear approximation, as shown in Figure 1. The expressions related to each of these approximations are Al(zA) = M−1 ∑ t=0 (1− M̂Rt)(F̂ARt+1 − F̂ARt) Au(zA) = M−1 ∑ t=0 (1− M̂Rt+1)(F̂ARt+1 − F̂ARt) Am(zA) = M−1 ∑ t=0 (1− M̂Rt + M̂Rt+1 2 )(F̂ARt+1 − F̂ARt). Substituting the estimates fo...

We reassess the predictability of U.S. recessions at horizons from three months to two years ahead for a large number of previously proposed leading-indicator variables. We employ an efficient probit estimator for partially missing data and assess relative model performance based on the receiver operating characteristic (ROC) curve. While the Treasury term spread has the highest predictive powe...

Because accurate diagnosis lies at the heart of medicine, it is important to be able to evaluate the effectiveness of diagnostic tests. A variety of accuracy measures are used. One particularly widely used measure is the AUC, the area under the receiver operating characteristic (ROC) curve. This measure has a well-understood weakness when comparing ROC curves which cross. However, it also has t...

The precision-recall plot is more informative than the ROC plot when evaluating classifiers on imbalanced datasets, but fast and accurate curve calculation tools for precision-recall plots are currently not available. We have developed Precrec, an R library that aims to overcome this limitation of the plot. Our tool provides fast and accurate precision-recall calculations together with multiple...

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The problem of learning from imbalanced data sets, while not the same problem as learning when misclassification costs are unequal and unknown, can be handled in a similar manner. That is, in both contexts, we can use techniques from roc analysis to help with classifier design. We present results from two studies in which we dealt with skewed data sets and unequal, but unknown costs of error. W...

Distributions have several different types of associated functions. The most familiar is the density function. Figure 1A and 1D show density functions for the normal distribution and gamma distribution, respectively. Density functions are related to histograms in that if enough data are collected then the histogram and density share the same shape. Hence, density functions serve as a first-orde...

Controversy exists regarding the clinical utility of pleural fluid pH, lactate dehydrogenase (LDH), and glucose for identifying complicated parapneumonic effusions that require drainage. In this report, we performed a meta-analysis of pertinent studies, using receiver operating characteristic (ROC) techniques, to assess the diagnostic accuracy of these tests, to determine appropriate decision t...

The paper deals with quality measures of whole sets of rules extracted from data, as a counterpart to more commonly used measures of individual rules. This research has been motivated by increasingly frequent extraction of non-classification rules, such as association rules and rules of observational logic, in real-world data mining tasks. The paer sketches the typology of rules extraction meth...

The aim of this study is to elaborate a tool, the “Naples-Questionnaire of Work Distress” (nQ-WD), in order to evaluate the conditions of discomfort perceived in the working field. It tries to differentiate the dysfunctional phenomena more tied to the anomalies of the interpersonal relationships (bullying at workplace) from the phenomena more clearly related to organizational dysfunctions. The ...