Bagging
Algorithms AIMech:Bagging DRAFT
Aliases: Bootstrap aggregating
Ensemble learning that aggregates predictors trained on bootstrap samples.
Identity Notes & Concept Scope
EnsembleLearning does not require bootstrap samples; bagging is distinct from generic ensembles and sampling without replacement.
Claims & Grounded Evidence
1 Claim
Mechanism
#mechanism
Base estimators are fitted on bootstrap samples and their predictions are aggregated.
⚠️ Curated Limitations & Negative Results
- This record supports method identity and the stated mechanism only; no universal performance or biological efficacy claim is curated.
- Proposed placement is an inference from the existing AIO hierarchy; maintainers must resolve task, algorithm and model scope before acceptance.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:EnsembleLearning
Ensemble Learning
|
related_match | EnsembleLearning does not require bootstrap samples; bagging is distinct from generic ensembles and sampling without replacement. Mapping is local curation, not an upstream subclass assertion. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
scikit-learn developers
|
official documentation
documentation
|
sha256:8014d31b6fc9…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T03:01:34Z
Updated: 2026-10-11T03:01:34Z
Source File:
data/curated/Bagging.yaml