This thesis demonstrates how to perform cost-sensitive binary classification in Azure ML Studio to predict credit risk based on the information given on a credit application. The classification problem in this experiment is a cost-sensitive one because the cost of misclassifying the positive samples is five times the cost of misclassifying the negative samples.
