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Linear discriminant analysis

Model Families AIMech:LinearDiscriminantAnalysis DRAFT

Aliases: LDA

Gaussian discriminant classification sharing one covariance matrix across classes.

Identity Notes & Concept Scope Classification does not specify Gaussian class models with shared covariance; distinguish the classifier from its dimension-reduction use.

Claims & Grounded Evidence

1 Claim

Mechanism #mechanism

Gaussian class-conditional distributions share a covariance matrix, producing linear decision boundaries.

supports Source: scikit-learn User Guide: lda qda · Locator: User Guide, section #lda

⚠️ 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.
The record concerns the Gaussian classifier; the dimensionality-reduction use is a related scope requiring separate assessment.

Artificial Intelligence Ontology (AIO) Grounding

Mapped AIO Classes

AIO Target Class	Mapping Relation	Mapping Rationale
`AIO:Classification` Classification	related_match	Classification does not specify Gaussian class models with shared covariance; distinguish the classifier from its dimension-reduction use. 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 User Guide: lda qda scikit-learn developers	official documentation documentation	sha256:367d42c265bf…

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/LinearDiscriminantAnalysis.yaml