Understanding ROC Curves

Bob Horton explains ROC curves and shows how to create them in R:

ROC curves are commonly used to characterize the sensitivity/specificity tradeoffs for a binary classifier. Most machine learning classifiers produce real-valued scores that correspond with the strength of the prediction that a given case is positive. Turning these real-valued scores into yes or no predictions requires setting a threshold; cases with scores above the threshold are classified as positive, and cases with scores below the threshold are predicted to be negative. Different threshold values give different levels of sensitivity and specificity. A high threshold is more conservative about labelling a case as positive; this makes it less likely to produce false positive results but more likely to miss cases that are in fact positive (lower rate of true positives). A low threshold produces positive labels more liberally, so it is less specific (more false positives) but also more sensitive (more true positives). The ROC curve plots true positive rate against false positive rate, giving a picture of the whole spectrum of such tradeoffs.

ROC curves are one of the primary techniques for figuring out if a binary classifier “works.”

Related Posts

Calculating Lifetime Value With R

Sergey Bryl shows how to calculate the lifetime value of a subscription service: Predicting LTV is a common issue for a new, recently launched product/service/application when we don’t have a lot of historical data but want to calculate LTV as soon as possible. Even though we may have a lot of historical data on customer […]

Read More

Interpreting The Area Under The Receiver Operating Characteristic Curve

Roos Colman explains what a Receiver Operating Characteristic (ROC) curve is and how we interpret the Area Under the Curve (AUC): The AUC can be defined as “The probability that a randomly selected case will have a higher test result than a randomly selected control”. Let’s use this definition to calculate and visualize the estimated […]

Read More

Categories

August 2016
MTWTFSS
« Jul Sep »
1234567
891011121314
15161718192021
22232425262728
293031