Smooth Receiver Operating Characteristics Curves (smROC)
published: Oct. 10, 2011, recorded: September 2011, views: 3623
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Supervised learning algorithms perform common tasks including classification, ranking, scoring, and probability estimation. We investigate how scoring information, often produced by these models, is utilized by an evaluation measure. The ROC curve represents a visualization of the ranking performance of classifiers. However, they ignore the scores which can be quite informative. While this ignored information is less precise than that given by probabilities, it is much more detailed than that conveyed by ranking. This paper presents a novel method to weight the ROC curve by these scores. We call it the Smooth ROC (smROC) curve, and we demonstrate how it can be used to visualize the performance of learning models. We report experimental results to show that the smROC is appropriate for measuring performance similarities and differences between learning models, and is more sensitive to performance characteristics than the standard ROC curve.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !