3.4. Metrics and scoring: quantifying the quality of predictions
A broad scoring reference that distinguishes classification, regression, ranking and clustering metrics and their scorer interfaces.
External resource. No execution or independent verification is claimed here.
Can a scoring implementation reproduce independently computed confusion-matrix and probability-loss examples, including undefined edge cases?
The evaluator owns small prediction tables and manually derives expected values; label ordering, weighting, averaging and undefined conventions are fixed in advance.
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. 3.4. Metrics and scoring: quantifying the quality of predictions. https://scikit-learn.org/stable/modules/model_evaluation.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.
Put this resource to work.
Test an analysis against data with known generating truth
Can the analysis distinguish finite-sample variation from leakage, missingness bias, or a covariance bug?
Open the brief RESEARCH BRIEF · 6 SOURCESBuild a baseline whose preprocessing can be audited
Does a more complicated classifier improve on a simple baseline under the same frozen split and training-only normalization?
Open the brief RESEARCH BRIEF · 5 SOURCESTurn a published ML replication into a bounded next check
Which specific claim in an existing label-smoothing or noisy-label replication can be mapped to accessible code, data and an affordable evaluation?
Open the brief