Is Scikit Learn Safe?
Scikit Learn — Nerq Trust Score 88.0/100 (A grade). Based on analysis of 2 trust dimensions, it is considered safe to use. Last updated: 2026-03-31.
Yes, Scikit Learn is safe to use. 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. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-31. Machine-readable data (JSON).
Is Scikit Learn safe?
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.
What is Scikit Learn's trust score?
Scikit Learn has a Nerq Trust Score of 88.0/100, earning a A grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Scikit Learn?
Scikit Learn's strongest signal is popularity at 95/100. No known vulnerabilities have been detected. It meets the Nerq Verified threshold of 70+.
What is Scikit Learn and who maintains it?
| Author | Unknown |
| Category | pypi |
| Source | N/A |
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 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.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 65/100 |
| 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.
What data does Scikit Learn collect?
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: Scikit Learn Privacy Report · Privacy review
Is Scikit Learn secure?
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
How we calculated this 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 (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.
Full methodology documentation · Machine-readable data (JSON API)
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.