#+date: <2017-01-11 04:12:52 +1300>
-#+filetags: R analysis economics forecasting
+#+filetags: :R:analysis:economics:forecasting:
+#+title: AUC and the economics of predictive modelling.
The strenght of a predictive, machine-learning model is often evaluated by quoting the area under the curve or AUC (or similarly the Gini coefficient). This AUC represents the area under the ROC line, which shows the trade-off between false positives and true positives for different cutoff values. Cutoff values enable the use of a regression model for classification purposes, by marking the value below and above which either of the classifier values is predicted. Models with a higher AUC (or a higher Gini coefficient) are considered better.