Data Forest logo
Home page  /  Glossary / 
ROC Curve

ROC Curve

An ROC (Receiver Operating Characteristic) Curve is a graphical plot illustrating the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. The area under the ROC curve (AUC) is a measure of the classifier's performance; a higher AUC indicates a better ability to distinguish between positive and negative classes. ROC curves are widely used to evaluate the performance of classification models, particularly in medical diagnostics, fraud detection, and other applications where distinguishing between two classes is critical.

Data Science
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Latest publications

All publications
Preview article image
October 4, 2024
18 min

Web Price Scraping: Play the Pricing Game Smarter

Article image preview
October 4, 2024
19 min

The Importance of Data Analytics in Today's Business World

Generative AI for Data Management: Get More Out of Your Data
October 2, 2024
20 min

Generative AI for Data Management: Get More Out of Your Data

All publications
top arrow icon