Why one model can't do two jobs
The post Two-Stage Hurdle Models: Predicting Zero-Inflated Outcomes appeared first on Towards Data Science.
Two-Stage Hurdle Models: Predicting Zero-Inflated Outcomes
Towards Data Science / 3/19/2026
💬 OpinionIdeas & Deep AnalysisModels & Research
Key Points
- Zero-inflated data require modeling two separate processes: whether an observation is zero and, if non-zero, the size of the outcome.
- Two-stage hurdle models separate these tasks, often improving interpretability and predictive performance compared to a single-model approach.
- The first stage uses a binary model (e.g., logistic) to predict zero versus non-zero occurrence, while the second stage models the positive outcomes using non-zero data only.
- This approach is particularly appropriate for datasets with excess zeros and potential overdispersion, and it should be compared with alternative methods like zero-inflated models to choose the best fit.
