Credit scoring with class imbalance data: An out-of-sample and out-of-time perspective.

Name: Credit scoring with class imbalance data: An out-of-sample and out-of-time perspective.
Creator: jonah mushava
Published: 2023-02-14T14:11:30.159Z
Keywords: Financial Risk, Machine Learning, Credit

mushava, jonah

doi:10.17632/bzr2rxttvz.1

Credit scoring with class imbalance data: An out-of-sample and out-of-time perspective.

Published: 14 February 2023| Version 1 | DOI: 10.17632/bzr2rxttvz.1

Contributor:

jonah mushava

Description

Data for predicting default risk in the next 12, 36, and 60 months. A training sample is provided for each outcome window, as well as out-of-sample (OOS) and out-of-time (OOT) testing samples. OOS is generated using the same timeline as the training sample, whereas OOT is created from a future timeline. The samples are all generated using the same characteristics/features.

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Institutions

University of KwaZulu-Natal - Westville Campus

Credit scoring with class imbalance data: An out-of-sample and out-of-time perspective.

Description

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Institutions

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