  In bankruptcy prediction, the proportion of events is very low, which is
often oversampled to eliminate this bias. In this paper, we study the influence
of the event rate on discrimination abilities of bankruptcy prediction models.
First the statistical association and significance of public records and
firmographics indicators with the bankruptcy were explored. Then the event rate
was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven
models were developed, including Logistic Regression, Decision Tree, Random
Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural
Network. Under different event rates, models were comprehensively evaluated and
compared based on Kolmogorov-Smirnov Statistic, accuracy, F1 score, Type I
error, Type II error, and ROC curve on the hold-out dataset with their best
probability cut-offs. Results show that Bayesian Network is the most
insensitive to the event rate, while Support Vector Machine is the most
sensitive.
