← Back to results Confidence Intervals for the Model Performance Metrics Under the Imbalanced Classification: Evaluating SMOTE’s Impact on Metrics’ Reliability Yury Y. Festa & Henry Penikas
What the paper says AI applications in finance including those for the probability of default modeling largely involve using ML classification tools. Oversampling the very minor (very underrepresented) class of defaulted borrowers seems to be a must-be-done step always. However, by crunching more than a thousand of confidence intervals for the classification accuracy metrics, we demonstrate when such oversampling is worth engaging in. Moreover, we argue to what portion of total initial sample size such oversampling should be carried out. Our findings are valuable primarily for the credit risk modeling and Internal Ratings Based (IRB) banks, but are not limited to those and have general applications for the binary classifications in ML domain.
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@article{yury2025,
title = {{Confidence Intervals for the Model Performance Metrics Under the Imbalanced Classification: Evaluating SMOTE’s Impact on Metrics’ Reliability}},
author = {Yury Y. Festa & Henry Penikas},
journal = {Model Assisted Statistics and Applications},
year = {2025},
doi = {https://doi.org/https://doi.org/10.1177/15741699251350368},
} TY - JOUR
TI - Confidence Intervals for the Model Performance Metrics Under the Imbalanced Classification: Evaluating SMOTE’s Impact on Metrics’ Reliability
AU - Festa, Yury Y.
AU - Penikas, Henry
JO - Model Assisted Statistics and Applications
PY - 2025
ER - Yury Y. Festa & Henry Penikas (2025). Confidence Intervals for the Model Performance Metrics Under the Imbalanced Classification: Evaluating SMOTE’s Impact on Metrics’ Reliability. *Model Assisted Statistics and Applications*. https://doi.org/https://doi.org/10.1177/15741699251350368 Yury Y. Festa & Henry Penikas. "Confidence Intervals for the Model Performance Metrics Under the Imbalanced Classification: Evaluating SMOTE’s Impact on Metrics’ Reliability." *Model Assisted Statistics and Applications* (2025). https://doi.org/https://doi.org/10.1177/15741699251350368. Confidence Intervals for the Model Performance Metrics Under the Imbalanced Classification: Evaluating SMOTE’s Impact on Metrics’ Reliability
Yury Y. Festa & Henry Penikas · Model Assisted Statistics and Applications · 2025
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