Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and chemometrics......click here for more. 
Home Multivariate Data Modeling Classification and Discrimination ROC Curve  
See also: Classifier Performance  
ROC Curve
In the case the classification performance depends on a decision threshold, we may plot the ROC values of a classifier for a set of different thresholds. The resulting ROC curve (see figure at right) is a simple but effective means for determining the properties of the classifier. First of all we may determine the optimum threshold by searching the ROC value which shows the largest perpendicular distance to the diagonal. Secondly, the area under the ROC curve (AUC) is a measure for the quality of the classifier. A classifier with no discriminating power yields a ROC curve which lies exactly on the diagonal. The higher the discriminating power the higher is AUC. The AUC can be interpreted as the probability of a positive value to be classified as positive.


Home Multivariate Data Modeling Classification and Discrimination ROC Curve 