Data Science#Non-Stationary Data, Prioritization, Binary Classification📝 BlogAnalyzed: Jan 3, 2026 07:01
Non-Stationary Categorical Data Prioritization
Published:Dec 23, 2025 09:23
•1 min read
•r/datascience
Analysis
The article describes a real-world problem of prioritizing items in a backlog where the features are categorical, the target is binary, and the scores evolve over time as more information becomes available. The core challenge is that the data is non-stationary, meaning the relationship between features and the target changes over time. The author is seeking advice on the appropriate modeling approach and how to handle training and testing to reflect the inference process. The problem is well-defined and highlights the complexities of using machine learning in dynamic environments.
Key Takeaways
- •The problem involves non-stationary data where item scores change over time as more information becomes available.
- •The goal is to prioritize items based on their current probability of success, not to predict future changes.
- •The author is seeking guidance on appropriate modeling and training/testing strategies for this scenario.
Reference
“The important part is that the model is not trying to predict how the item evolves over time. Each score is meant to answer a static question: “Given everything we know right now, how should this item be prioritized relative to the others?””