هل Scikit Learn آمن؟
Scikit Learn — Nerq Trust Score 88.0/100 (الدرجة A). بناءً على تحليل 2 أبعاد للثقة، يُعتبر آمنًا للاستخدام. آخر تحديث: 2026-03-31.
نعم، Scikit Learn آمن للاستخدام. Scikit Learn is a 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: 65/100. Popularity: 95/100. البيانات مصدرها قراءة آلية.
هل Scikit Learn آمن؟
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.
ما هي درجة ثقة Scikit Learn؟
حصل Scikit Learn على درجة ثقة Nerq تبلغ 88.0/100 بدرجة A. يعتمد هذا التقييم على 2 أبعاد مُقاسة بشكل مستقل.
ما هي النتائج الأمنية الرئيسية لـ Scikit Learn؟
أقوى إشارة لـ Scikit Learn هي الشعبية بدرجة 95/100. لم يتم اكتشاف أي ثغرات أمنية معروفة. يستوفي عتبة التحقق من Nerq البالغة 70+.
ما هو Scikit Learn ومن يديره؟
| المؤلف | Unknown |
| الفئة | pypi |
| المصدر | N/A |
دليل الأمان: Scikit Learn
ما هو 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 62/100 (C+) and has not yet reached Nerq trust threshold (70+). This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Scikit Learn has a Trust Score of 62/100 (C+).
- Review carefully before use — below trust threshold.
- Always verify independently using the Nerq API.
تحليل مفصل للدرجة
| Dimension | Score |
|---|---|
| Security | 65/100 |
| الخصوصية | 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.
ما البيانات التي يجمعها 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.
Full analysis: تقرير الخصوصية · الخصوصية review
هل Scikit Learn آمن؟
Security score: 65/100. 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.
Full analysis: Scikit Learn Security Report
كيف حسبنا هذه الدرجة
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 (65/100), 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 March 31, 2026. Data version: 1.0.
الأسئلة الشائعة
Is Scikit Learn safe to use?
ما هو Scikit Learn's trust score?
What are safer alternatives to Scikit Learn?
Does Scikit Learn have known vulnerabilities?
How actively maintained is Scikit Learn?
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Disclaimer: درجات ثقة Nerq هي تقييمات آلية مبنية على إشارات متاحة للعموم. وهي ليست توصيات أو ضمانات. قم دائمًا بإجراء العناية الواجبة الخاصة بك.