Figure 4.

Using precision-recall curves to evaluate overfitting

Precision-recall curves illustrating overfit, better fit, and underfit models. A substantial difference between Test and Train precision-recall curves suggests overfitting, and that the max number of features should likely be lowered to improve model performance. When a model is overfit, as features are removed, the Test precision-recall curve will shift right, while the training curve is minimally affected. Once too many features have been removed, performance in both test and training will decline.