The Receiver Operating Characteristic (ROC) curve is a two dimensional measure of classification performance. The area under the ROC curve (AUC) is a scalar measure gauging one facet of performance. In this note, five idealized models are utilized to relate the shape of the ROC curve, and the area under it, to features of the underlying distribution of forecasts. This allows for an interpretati...

Introduction Clozapine has been well established as the most efficacious medication for treatment refractory schizophrenia. Optimising benefit during clozapine trial is an important clinical consideration. Therapeutic drug monitoring of plasma or serum levels formed a critical part this. Though there no agreed standardised therapeutic range, advice traditionally recommends level >350ng/mL in...

ROC curves are one of the most widely used displays to evaluate performance of scoring functions. In the paper, we propose a statistical method for directly optimizing the ROC curve. The target is known to be the regression function up to an increasing transformation and this boils down to recovering the level sets of the latter. We propose to use classifiers obtained by empirical risk minimiza...

In the practice of credit-risk management, models for receiver operating characteristic (ROC) curves are helpful in describing shape an ROC curve, estimating discriminatory power a scorecard, and generating without underlying data. The primary purpose this study is to review curve proposed literature, primarily biostatistics, fit them actual credit-scoring data order determine which could be us...

multiple diagnostic tools are used by emergency physicians, every day. in addition, new tools are evaluated to obtain more accurate methods and reduce time or cost of conventional ones. in the previous parts of this educational series, we described diagnostic performance characteristics of diagnostic tests including sensitivity, specificity, positive and negative predictive values, and likeliho...

Abstract The development of medical diagnostic tests is of great importance in clinical practice, public health, and medical research. The receiver operating characteristic (ROC) curve is a popular tool for evaluating the accuracy of such tests. We review Bayesian nonparametric methods based on Dirichlet process mixtures and the Bayesian bootstrap for ROC curve estimation and regression. The me...

For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassification costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more meaningful performance measures. In this paper, we propose a quadratic programming bas...