Scikit Learn è sicuro?
Scikit Learn — Nerq Trust Score 88.0/100 (Grado A). Sulla base dell'analisi di 2 dimensioni di fiducia, è considerato sicuro da usare. Ultimo aggiornamento: 2026-04-05.
Sì, Scikit Learn è sicuro da usare. Scikit Learn è un 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. Dati leggibili dalle macchine (JSON).
Scikit Learn è sicuro?
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.
Qual è il punteggio di fiducia di Scikit Learn?
Scikit Learn ha un Nerq Trust Score di 88.0/100 con voto A. Questo punteggio si basa su 2 dimensioni misurate indipendentemente, tra cui sicurezza, manutenzione e adozione della community.
Quali sono i risultati di sicurezza chiave per Scikit Learn?
Il segnale più forte di Scikit Learn è popolarità a 100/100. Non sono state rilevate vulnerabilità note. It meets the Nerq Verified threshold of 70+.
Cos'è Scikit Learn e chi lo mantiene?
| Autore | Unknown |
| Categoria | pypi |
| Fonte | N/A |
Pypi simili per Punteggio di Fiducia
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.
Analisi dettagliata del punteggio
| 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.
Quali dati raccoglie 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.
Analisi completa: Report sulla privacy di Scikit Learn · Privacy review
Scikit Learn è sicuro?
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.
Analisi completa: Report di sicurezza di Scikit Learn
Come abbiamo calcolato questo punteggio
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)
Domande frequenti
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Disclaimer: I punteggi di fiducia Nerq sono valutazioni automatizzate basate su segnali disponibili pubblicamente. Non costituiscono raccomandazioni o garanzie. Effettua sempre la tua verifica personale.