Czy Scikit Learn jest bezpieczny?
Scikit Learn — Nerq Trust Score 88.0/100 (Ocena A). Na podstawie analizy 2 wymiarów zaufania, jest uważany za bezpieczny w użyciu. Ostatnia aktualizacja: 2026-04-05.
Tak, Scikit Learn jest bezpieczny w użyciu. Scikit Learn to 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. Dane odczytywalne maszynowo (JSON).
Czy Scikit Learn jest bezpieczny?
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.
Jaki jest wynik zaufania Scikit Learn?
Scikit Learn ma Nerq Trust Score 88.0/100 z oceną A. Ten wynik opiera się na 2 niezależnie mierzonych wymiarach, w tym bezpieczeństwie, konserwacji i adopcji społeczności.
Jakie są kluczowe ustalenia bezpieczeństwa dla Scikit Learn?
Najsilniejszy sygnał Scikit Learn to popularność na poziomie 100/100. Nie wykryto znanych luk w zabezpieczeniach. It meets the Nerq Verified threshold of 70+.
Czym jest Scikit Learn i kto go utrzymuje?
| Autor | Unknown |
| Kategoria | pypi |
| Źródło | N/A |
Podobne Pypi wg wyniku zaufania
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.
Szczegółowa analiza wyniku
| 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.
Jakie dane zbiera 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.
Pełna analiza: Raport prywatności Scikit Learn · Privacy review
Czy Scikit Learn jest bezpieczny?
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.
Pełna analiza: Raport bezpieczeństwa Scikit Learn
Jak obliczyliśmy ten wynik
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: Wyniki zaufania Nerq to zautomatyzowane oceny oparte na publicznie dostępnych sygnałach. Nie stanowią rekomendacji ani gwarancji. Zawsze przeprowadzaj własną weryfikację.