Setting the threshold of a binary learning model in Azure ML

This is the last 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 final article will cover the threshold setting, and how to find the optimal value for it. As you will learn, this requires a good understanding of error cost, that is, the cost of inaccurate predictions.