---
title: Active Learning for Ensemble Models
url: https://www.ml-quant.com/papers/repec/gam-jstats-v-7-y-2024-i-1-p-8-137-d-1325699/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:gam:jstats:v:7:y:2024:i:1:p:8-137:d:1325699
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2571-905X%2F7%2F1%2F8%2Fpdf%3Bh%3Drepec%3Agam%3Ajstats%3Av%3A7%3Ay%3A2024%3Ai%3A1%3Ap%3A8-137%3Ad%3A1325699
featured: 2024-01-30
citations: unknown
topic: ML & AI Methods
---


# Active Learning for Ensemble Models

Active learning within ensemble learning can achieve similar predictive performance on a limited budget, with boosting or stacking models outperforming the SVM model when using the same uncertainty sampling.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2571-905X%2F7%2F1%2F8%2Fpdf%3Bh%3Drepec%3Agam%3Ajstats%3Av%3A7%3Ay%3A2024%3Ai%3A1%3Ap%3A8-137%3Ad%3A1325699
- Identifier: RePEc:gam:jstats:v:7:y:2024:i:1:p:8-137:d:1325699
- Released: 2024-01-30
- First featured: Quant Letter No. 35 (2024-01-30): https://www.ml-quant.com/issues/2024-01-30/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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