On Imbalanced Regression with Hoeffding Trees

📰 ArXiv cs.AI

Learn to handle imbalanced regression with Hoeffding Trees and improve predictions using kernel density estimation and hierarchical shrinkage

advanced Published 2 Jun 2026
Action Steps
  1. Apply kernel density estimation to improve smoothed predictions in imbalanced regression
  2. Use hierarchical shrinkage for post-hoc regularization of Hoeffding Trees
  3. Implement Hoeffding Trees with KDE and HS for handling imbalanced regression
  4. Evaluate the performance of Hoeffding Trees with KDE and HS on imbalanced datasets
  5. Compare the results with traditional Hoeffding Trees without KDE and HS
Who Needs to Know This

Data scientists and machine learning engineers working with continuous data streams can benefit from this research to improve their regression models

Key Insight

💡 Kernel density estimation and hierarchical shrinkage can improve the performance of Hoeffding Trees in imbalanced regression

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Improve imbalanced regression with Hoeffding Trees using kernel density estimation and hierarchical shrinkage #machinelearning #regression

Key Takeaways

Learn to handle imbalanced regression with Hoeffding Trees and improve predictions using kernel density estimation and hierarchical shrinkage

Full Article

Title: On Imbalanced Regression with Hoeffding Trees

Abstract:
arXiv:2602.22101v3 Announce Type: replace-cross Abstract: Many real-world applications generate continuous data streams for regression. Hoeffding trees and their variants have a long-standing tradition due to their effectiveness, either alone or as base models in broader ensembles. Recent batch-learning work shows that kernel density estimation (KDE) improves smoothed predictions in imbalanced regression [Yang et al., 2021], while hierarchical shrinkage (HS) provides post-hoc regularization for
Read full paper → ← Back to Reads

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