How is ROC area calculated in Weka?

How is ROC area calculated in Weka?

The Weka Explorer enables you to plot the ROC (Receiver operating characteristic) curve for a certain class label of dataset:

1. run a classifier on a dataset.
2. right-click in the result list on the result you want to display the curve for.
3. select Visualize threshold curve and choose the class label you want the plot for.

What is the area under ROC curve?

The Area Under the ROC curve (AUC) is a measure of how well a parameter can distinguish between two diagnostic groups (diseased/normal). MedCalc creates a complete sensitivity/specificity report. The ROC curve is a fundamental tool for diagnostic test evaluation.

What is ROC in data mining?

An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters: True Positive Rate. False Positive Rate.

What is a good AUC for ROC?

AREA UNDER THE ROC CURVE In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.

How to calculate the sample size using ROC?

The algorithm for sample size determination using confidence estimates of ROC involves the following steps: 1. Select indicator of models’ performance: ROC points, AUC. 2. Select methodof sample size increment. 3. Calculate confidence intervals for a chosen ROC parameter using either bayesian or frequentist ap-proach.

What do you need to know about the ROC curve?

ROC curve analysis What is a ROC curve? A ROC curve is a plot of the true positive rate (Sensitivity) in function of the false positive rate (100-Specificity) for different cut-off points of a parameter. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold.

Which is the best introduction to ROC analysis?

An introduction to ROC analysis. Tom Fawcett. Institute for the Study of Learning and Expertise, 2164 Staunton Court, Palo Alto, CA 94306, USA Available online 19 December 2005. Abstract Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance.

How are abstract receiver operating characteristics ( ROC ) used?

Abstract Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research.