Binary classfication: credit risk prediction - Bobriiekhova, K. M.; Bocharov, B. P. (НТУ ДП, 2021)
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.
Keywords: бинарная система; бінарна система; кредитний ризик; прогнозування; прогнозирование; научная публикация НГУ 2021