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RECENT POSTS
- The Workflow of Infinite Shame, and other stories from the R Summit
- What is a good Sharpe ratio?
- Open Analytics @ UseR! 2015
- When did same-sex marriage laws become effective by state?
- Bio7 2.2 for Windows, Linux and MacOSX released
- Rperform: Obtaining quantitative metrics across R package versions
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- R at Microsoft
- Introduction to Monte Carlo methods with R and Bayesian Essentials with R
- An Attempt to Understand Boosting Algorithm(s)
- remote update, paper & poster
- Geomorph update 2.1.6 Now Available!
- KDD Cup 2015: The story of how I built hundreds of predictive models….And got so close, yet so far away from 1st place!
- Working with the RStudio CRAN logs
- Learn to Map Census Data in R
OTHER SITES
Illustrated Guide to ROC and AUCJune 23, 2015
By Raffael Vogler
(This article was first published on joy of data » R, and kindly contributed toR-bloggers)
(In a past job interview I failed at explaining how tocalculate and interprete ROC curves– so here goes my attempt to fill this knowledge gap.) Think of a regression modelmapping a number of features onto a real number (potentially a probability). The resulting real number can then be mapped on one of two classes, depending on whether this predicted number is greater or lower than some choosable threshold. Let’s take for example a logistic regression and data on the survivorship of the Titanic accident to introduce the relevant concepts which will lead naturally to the ROC (Receiver Operating Characteristic) and its AUC or AUROC (Area Under ROC Curve).
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