3 papers
stat.ML2025
Analyzing decision tree bias towards the minority class
Nathan Phelps, Daniel J. Lizotte, Douglas G. Woolford
There is a widespread and longstanding belief that machine learning models are biased towards the majority class when learning from imbalanced binary response data, leading them to…
cs.LG2024
Challenges in the calibration of tree-based models for imbalanced classification
Nathan Phelps, Daniel J. Lizotte, Douglas G. Woolford
When using machine learning for imbalanced binary classification problems, it is common to subsample the majority class to create a (more) balanced training dataset. This biases th…
stat.ME2024
Using Platt's scaling for calibration after undersampling -- limitations and how to address them
Nathan Phelps, Daniel J. Lizotte, Douglas G. Woolford
When modelling data where the response is dichotomous and highly imbalanced, response-based sampling where a subset of the majority class is retained (i.e., undersampling) is often…