arXiv subjects

Douglas G. Woolford

Publications and source records attributed to Douglas G. Woolford.

4 recordsLinked to original sources

arXiv2026

Challenges in the calibration of tree-based models for imbalanced classification

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 the model's predictions because the model learns from data that is not fully representative of the underlying population of interest. One way of accounting for this bias is analytically mapping the resulting predictions to new values based on the sampling rate for the majority class. We show that calibrating a random forest this way has negative consequences, including prevalence estimates that depend on both the number of predictors considered at each split in the random forest and the sampling rate used. We explain the former using known properties of random forests and analytical calibration and the latter by demonstrating a bias in decision trees. In contradiction with much of the existing literature, we show that decision trees can be biased towards the minority class. These issues indicate that tree-based models trained on undersampled data should not be calibrated analytically. Calibration approaches that can learn a miscalibration pattern in the original model (e.g., beta calibration) are more suitable.

cs.LG↗

arXiv2026

Analyzing decision tree bias towards the minority class

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 neglect or ignore the minority class. Motivated by a recent simulation study that found that decision trees can be biased towards the minority class, our paper aims to reconcile the conflict between that study and other published works. First, we critically evaluate past literature on this problem, finding that failing to consider the conditional distribution of the outcome given the predictors has led to incorrect conclusions about the bias in decision trees. We then show that, under specific conditions, decision trees fit to purity are biased towards the minority class, debunking the belief that decision trees are always biased towards the majority class. This bias can be reduced by adjusting the tree-fitting process to include regularization methods like pruning and setting a maximum tree depth, and/or by using post-hoc calibration methods. Our findings have implications on the use of popular tree-based models, such as random forests. Although random forests are often composed of decision trees fit to purity, our work adds to recent literature indicating that this may not be the best approach.

stat.ML↗

arXiv2024

Using Platt's scaling for calibration after undersampling -- limitations and how to address them

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 used to create more balanced training datasets prior to modelling. However, the models fit to this undersampled data, which we refer to as base models, generate predictions that are severely biased. There are several calibration methods that can be used to combat this bias, one of which is Platt's scaling. Here, a logistic regression model is used to model the relationship between the base model's original predictions and the response. Despite its popularity for calibrating models after undersampling, Platt's scaling was not designed for this purpose. Our work presents what we believe is the first detailed study focused on the validity of using Platt's scaling to calibrate models after undersampling. We show analytically, as well as via a simulation study and a case study, that Platt's scaling should not be used for calibration after undersampling without critical thought. If Platt's scaling would have been able to successfully calibrate the base model had it been trained on the entire dataset (i.e., without undersampling), then Platt's scaling might be appropriate for calibration after undersampling. If this is not the case, we recommend a modified version of Platt's scaling that fits a logistic generalized additive model to the logit of the base model's predictions, as it is both theoretically motivated and performed well across the settings considered in our study.

stat.ME↗

arXiv2013

Wildfire Prediction to Inform Fire Management: Statistical Science Challenges

Wildfire is an important system process of the earth that occurs across a wide range of spatial and temporal scales. A variety of methods have been used to predict wildfire phenomena during the past century to better our understanding of fire processes and to inform fire and land management decision-making. Statistical methods have an important role in wildfire prediction due to the inherent stochastic nature of fire phenomena at all scales. Predictive models have exploited several sources of data describing fire phenomena. Experimental data are scarce; observational data are dominated by statistics compiled by government fire management agencies, primarily for administrative purposes and increasingly from remote sensing observations. Fires are rare events at many scales. The data describing fire phenomena can be zero-heavy and nonstationary over both space and time. Users of fire modeling methodologies are mainly fire management agencies often working under great time constraints, thus, complex models have to be efficiently estimated. We focus on providing an understanding of some of the information needed for fire management decision-making and of the challenges involved in predicting fire occurrence, growth and frequency at regional, national and global scales.

stat.AP↗