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A Descriptive Study of Variable Discretization and Cost-Sensitive Logistic Regression on Imbalanced Credit Data
Training classification models on imbalanced data tends to result in bias towards the majority class.
Logistic Ensemble Models
Predictive models that are developed in a regulated industry or a regulated application, like determination of credit worthiness, must be interpretable and rational (e. g., meaningful improvements in basic credit behavior must result in improved credit worthiness scores).
Influence of the Event Rate on Discrimination Abilities of Bankruptcy Prediction Models
In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias.
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