Elastic net
Mathematical Methods AIMech:ElasticNet DRAFT
Aliases: Elastic-net regression
Linear regression regularized by a combination of L1 and L2 penalties.
Identity Notes & Concept Scope
RegressionAnalysis does not require mixed L1/L2 penalties; existing Lasso and Ridge concepts cover the separate penalty types.
Claims & Grounded Evidence
1 Claim
Mechanism
#mechanism
The fitting objective combines a squared-error term with L1 and squared L2 penalties on coefficients.
⚠️ 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:RegressionAnalysis
Regression Analysis
|
related_match | RegressionAnalysis does not require mixed L1/L2 penalties; existing Lasso and Ridge concepts cover the separate penalty types. 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
|
sha256:358fa62c5481…
|
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/ElasticNet.yaml