Binary classfication: credit risk prediction
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Дата
2021Автор
Bobriiekhova, K. M.
Bocharov, B. P.
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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.