Machine learningMachine learning

Active Learning Gradient Boosting

Active Learning Gradient Boosting combines the powerful predictive accuracy of gradient boosted trees with an active learning loop that selects the most informative unlabeled examples for human annotation. By querying only the instances the model is most uncertain about, the method achieves high accuracy with far fewer labeled examples than passive supervised learning.

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Sources

  1. Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison. link
  2. Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI: 10.1214/aos/1013203451

Related methods

ScholarGateActive Learning Gradient Boosting (Active Learning with Gradient Boosting (Query-by-Committee / Uncertainty Sampling with Gradient Boosted Trees)). Retrieved 2026-06-04 from https://scholargate.app/en/machine-learning/active-learning-gradient-boosting