Er Scikit Learn sikker?
Scikit Learn — Nerq Trust Score 88.0/100 (Karakter A). Baseret på analyse af 2 tillidsdimensioner vurderes det som sikkert at bruge. Sidst opdateret: 2026-04-05.
Ja, Scikit Learn er sikker at bruge. Scikit Learn er en Python package with a Nerq Trust Score of 88.0/100 (A), based on 3 independent data dimensions. It is recommended for production use. Security: 90/100. Popularity: 100/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-05. Maskinlæsbare data (JSON).
Er Scikit Learn sikker?
YES — Scikit Learn has a Nerq Trust Score of 88.0/100 (A). It meets Nerq's trust threshold with strong signals across security, maintenance, and community adoption. Recommended for production use — review the full report below for specific considerations.
Hvad er Scikit Learns tillidsscore?
Scikit Learn har en Nerq Trust Score på 88.0/100 med karakteren A. Denne score er baseret på 2 uafhængigt målte dimensioner, herunder sikkerhed, vedligeholdelse og community-adoption.
Hvad er de vigtigste sikkerhedsresultater for Scikit Learn?
Scikit Learns stærkeste signal er popularitet på 100/100. Ingen kendte sårbarheder er fundet. It meets the Nerq Verified threshold of 70+.
Hvad er Scikit Learn og hvem vedligeholder det?
| Udvikler | Unknown |
| Kategori | pypi |
| Kilde | N/A |
Lignende Pypi efter tillidsscore
Safety Guide: Scikit Learn
What is Scikit Learn?
Scikit Learn is a Python package — A set of python modules for machine learning and data mining.
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=scikit-learn
Key Safety Concerns for Python packages
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Scikit Learn has a Nerq Trust Score of 76/100 (B+) and meets Nerq trust threshold. This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Scikit Learn has a Trust Score of 76/100 (B+).
- Recommended for use — passes trust threshold.
- Always verify independently using the Nerq API.
Detaljeret scoreanalyse
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Privacy | 80/100 |
| Reliability | 90/100 |
| Transparency | 50/100 |
| Maintenance | 60/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
Hvilke data indsamler Scikit Learn?
Scikit Learn is a Python package maintained by Unknown. It receives approximately 45,807,671 weekly downloads.
As a development package, Scikit Learn does not directly collect end-user personal data. However, applications built with it may collect data depending on implementation. Privacy score: 80/100.
Review the package's dependencies for potential supply chain risks. Run your package manager's audit command regularly.
Fuld analyse: Scikit Learn privatlivsrapport · Privacy review
Er Scikit Learn sikker?
Security score: 90/100. Scikit Learn has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
License information not available. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Fuld analyse: Scikit Learn sikkerhedsrapport
Sådan beregnede vi denne score
Scikit Learn's trust score of 88.0/100 (A) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), privacy (80/100), reliability (90/100), transparency (50/100), maintenance (60/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
This page was last reviewed on April 05, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
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Disclaimer: Nerqs tillidsscorer er automatiserede vurderinger baseret på offentligt tilgængelige signaler. De udgør ikke anbefalinger eller garantier. Foretag altid din egen verificering.