Partial least squares regression
Mathematical Methods AIMech:PartialLeastSquaresRegression DRAFT
Aliases: PLS regression, PLSR
Regression using latent components chosen to capture predictor-response covariance.
Identity Notes & Concept Scope
RegressionAnalysis does not identify covariance-based latent predictor/response components.
Claims & Grounded Evidence
1 Claim
Mechanism
#mechanism
Predictors and responses are projected onto latent components chosen using their cross-covariance.
⚠️ 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 identify covariance-based latent predictor/response components. 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:d73a2c045776…
|
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/PartialLeastSquaresRegression.yaml