area under the curve

Terms from Artificial Intelligence: humans at the heart of algorithms

The glossary is being gradually proof checked, but currently has many typos and misspellings.

The area under the curve is an accuracy measure and is literally the area under the ROC curve. If a decision rule has higher precision or higher recall it ends up with a larger area under the curve. A random classifer has an area under the curve of 0.5, wheras a perfect classifier with 100% precsion and 100% recall has an area undet the curve of 1. In general, because of the precision–recall trade-off the value lies somewhere between the two.

Used in Chap. 9: pages 121, 130

ROC – choosing between classifiers.