Machine Learning on a Large Scale

The ROC curve is also used in order to compute the area under the ROC curve metric. The ROC curve of a perfect model will approach the top-left corner, whilst a random model will approach the diagonal (True positive rate False positive rate). The area under the ROC curve ranges between 0. and 1 and can be computed via a BinaryClassificationEvaluator object The result is impressive, despite the attempt to hamper the model quality. The area under the ROC curve for the training set can be obtained from the model summary lr_model.summary.areaUnderROC. The BinaryClassificationEvaluator object can also be used to compute the area under the PR curve.