# Obuchowski and Rockette method

## Obuchowski and Rockette method :

The Obuchowski and Rockette method is a statistical method for evaluating the accuracy of a diagnostic test or classifier. It is based on the receiver operating characteristic (ROC) curve, which is a graphical plot that illustrates the diagnostic ability of a test or classifier.
To understand the Obuchowski and Rockette method, it is helpful to first understand the concept of a ROC curve.
A ROC curve plots the true positive rate (sensitivity) against the false positive rate (1 – specificity) for different possible thresholds of the diagnostic test or classifier. The true positive rate is the proportion of actual positive cases that are correctly identified by the test or classifier. The false positive rate is the proportion of actual negative cases that are incorrectly identified as positive by the test or classifier.
For example, consider a diagnostic test for a certain disease. The true positive rate would be the proportion of patients with the disease who test positive, while the false positive rate would be the proportion of healthy patients who test positive.
The ROC curve is a useful tool for evaluating the accuracy of a diagnostic test or classifier because it allows you to compare the trade-off between the true positive rate and false positive rate at different thresholds. A test or classifier with a high true positive rate and low false positive rate is considered to be more accurate.
The Obuchowski and Rockette method is a method for comparing the accuracy of two or more diagnostic tests or classifiers based on their ROC curves. It involves calculating the area under the ROC curve (AUC) for each test or classifier, and then comparing the AUCs to determine which test or classifier is more accurate.
The AUC is a measure of the overall accuracy of a test or classifier, with a value of 0.5 indicating no accuracy and a value of 1 indicating perfect accuracy. A test or classifier with a higher AUC is considered to be more accurate than one with a lower AUC.
Here are two examples of how the Obuchowski and Rockette method can be used:
Example 1:
Suppose you are trying to evaluate the accuracy of two different diagnostic tests for a certain disease. Test A has an AUC of 0.75, while Test B has an AUC of 0.80. According to the Obuchowski and Rockette method, Test B is more accurate than Test A because it has a higher AUC.
Example 2:
Suppose you are trying to evaluate the accuracy of two different classifiers for identifying spam emails. Classifier A has an AUC of 0.65, while Classifier B has an AUC of 0.70. According to the Obuchowski and Rockette method, Classifier B is more accurate than Classifier A because it has a higher AUC.
In summary, the Obuchowski and Rockette method is a statistical method for evaluating the accuracy of diagnostic tests or classifiers based on their ROC curves. It involves calculating the AUC for each test or classifier and comparing the AUCs to determine which test or classifier is more accurate.