Home / Curated / Algorithms / Gradient boosting machine

Gradient boosting machine

Algorithms AIMech:GradientBoosting DRAFT

Aliases: Gradient boosting, GBM

Boosting that adds predictors fitted to negative loss-function gradients.

Identity Notes & Concept Scope EnsembleLearning does not specify stagewise fitting to negative loss gradients.

Claims & Grounded Evidence

1 Claim

Mechanism #mechanism

Each stage fits a learner to negative loss gradients evaluated at the current additive predictor.

supports Source: scikit-learn User Guide: ensemble · Locator: User Guide, section #mathematical-formulation

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. The inspected implementation is gradient tree boosting; the generic GBM scope needs the original functional-gradient formulation.

Mapped AIO Classes

AIO Target Class	Mapping Relation	Mapping Rationale
`AIO:EnsembleLearning` Ensemble Learning	related_match	EnsembleLearning does not specify stagewise fitting to negative loss gradients. Mapping is local curation, not an upstream subclass assertion.

1 Source

Source ID	Publication / Document	Type / Access	Reference & Checksum
`method-source`	scikit-learn User Guide: ensemble scikit-learn developers	official documentation documentation	sha256:8014d31b6fc9…

Curator: Codex

Method: ai_assisted

Model: GPT-6

Created: 2026-10-11T03:01:34Z

Updated: 2026-10-11T03:01:34Z

Source File: data/curated/GradientBoosting.yaml