Correcting Split Selection in Online Decision Trees via Anytime-Valid Inference
Bagging-based ensembles, most notably Adaptive Random Forests, are among the strongest performers for learning from data streams. A common denominator across these methods is their reliance on Hoeffding Trees as base learners, which grow decision trees incrementally by testing whether a candidate split is significantly better than its alternatives using concentration inequalities. Despite their empirical success, existing variants lack valid statistical guarantees. Current analyses rely on fixed-sample concentration bounds, while split decisions are made using data-dependent stopping rules, which invalidates their guarantees and can drive the probabilty of incorrect splits to one. We intr...
| Authors | Salim I. Amoukou, Saumitra Mishra, Manuela Veloso |
|---|---|
| Primary category | stat.ML |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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