RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE

Abstract

Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands. Receiving Operating Characteristic (ROC) curves are bidimensional graphs commonly used to evaluate and compare the performance of classifiers. ROC plots nicely show the sensitivity/specificity trade-off of a classifier for all possible classification thresholds, thus allowing the ranking and selection of classifiers according to specific user needs that often are associated with differential error costs and accuracy demands.