Test-Driven Machine Learning

Quantifying the classification models

To make sure that we're on the same page, let's start by looking at an example of an ROC curve and the AUC score. The scikit-learn documentation has an example code to build an ROC curve and calculate AUC, which you can find at http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html.

This ROC curve was built by running a classifier over the famous iris dataset. It shows us the true positive rate (y-axis) that we can get if we allow a given amount of false positive rate (x-axis). For example, if we were good with a 50 percent false positive rate, we would expect to see somewhere around a 90 ...