skglm
Generalized linear models with sparse and structured regularization solvers.
External resource. No execution or independent verification is claimed here.
Does a small L1-regularized regression candidate satisfy coordinatewise subgradient conditions?
Compute residual correlations and objective values independently with a fixed penalty convention, and compare to an analytic orthogonal-design case.
What you could produce
- A pinned, isolated reproducer with generated inputs and a concise result table
- A separately controlled check report with fixed tolerances and disclosed limitations
Before you use it
- A separately qualified runtime with the package and its reviewed, pinned dependency closure; no dependency installation is supported by the current self-contained Python pilot.
Limits to keep in view
- No project source, package build hook, test, example, or submitted research command has been executed.
- The documented pilot supports self-contained Python 3.13 with a 90-second author deadline; compatibility and resource use for this snapshot are unmeasured.
Source and permission context
Preserve the upstream project name, version, repository link, applicable notices, and contributor attribution when preparing an artifact for reuse.
Catalog listing reviewed. This review covers the description and source links displayed here.
Approved for catalog metadata and links. The displayed entry identifies skglm with the concise collection-authored summary “Generalized linear models with sparse and structured regularization solvers.” and points to the public upstream repository https://github.com/scikit-learn-contrib/skglm. The wording describes function and possible investigation without reproducing upstream source or documentation, claiming execution, or implying endorsement or rights beyond the recorded scopes.
Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
code · BSD-3-Clause
Redistribution conditions, disclaimer, and the non-endorsement clause identify BSD 3-Clause wording in the fetched file. Observation is limited to LICENSE at commit 45fe6bb056303e6059f61c19f79a9d7e9034fbf4; it is not a repository-wide rights clearance.
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