Performance measures in Azure ML: Accuracy, Precision, Recall and F1 Score.

This is the first of three articles about performance measures and graphs for binary learning models in Azure ML. Binary learning models are models which just predict one of two outcomes: positive or negative. These models are very well suited to drive decisions, such as whether to administer a patient a certain drug or to include a lead in a targeted marketing campaign. This first article lays the foundation by covering several statistical measures: accuracy, precision, recall and F1 score, These measures require a solid understanding of the two types of prediction errors which we will also cover: false positives and false negatives. In the second article we'll discuss the ROC curve and the related AUC measure. We'll also look at another graph in Azure ML called the Precision/Recall curve.