2.7. Novelty and Outlier Detection
A guide distinguishing outlier detection from novelty detection and comparing assumptions of several anomaly-detection methods.
External resource. No execution or independent verification is claimed here.
How does training-set contamination alter detection rates at a fixed false-positive target?
The evaluator owns the inlier and anomaly generators, preserves a clean calibration split and independently calculates the full operating curve.
What you could produce
- A versioned minimal protocol, evaluator-owned test cases, and a comparison report with numerical/statistical uncertainty and failures.
Before you use it
- scikit-learn 1.9.1
- Compatible NumPy/SciPy and compiled dependencies; exact environment not prepared
Limits to keep in view
- No upstream example, source package, build hook or submitted code was executed.
- The current qualified pilot accepts only self-contained Python 3.13 with a 90-second deadline. This reference's package/runtime is not qualified for that path.
- The proposed protocol requires bounded resource estimates and an independently controlled evaluator before any scientific execution claim.
Source and permission context
scikit-learn documentation. 2.7. Novelty and Outlier Detection. https://scikit-learn.org/stable/modules/outlier_detection.html; observed version 1.9.1 (observed documentation version; stable URL is mutable).
Rights need review. Review the scope and upstream conditions before reuse.
documentation · BSD-3-Clause
This documentation page credits the developers and explicitly labels its footer BSD License; the matching 1.9.1 COPYING file identifies the three-clause terms. Referenced datasets and separately licensed material are not cleared.
Inspect the license evidence ↗Still unresolved
- Third-party figures, linked papers, datasets and dependency licenses have not been assessed; no external content is copied into this catalog.