xgboost

Name: xgboost
Rating: 3.24 (1 reviews)
Author: dmlc

#803 · by dmlc

infra serverless GPU

3.24/ 5.00

trustedBeta

Mar 3, 2026 at 7:21 AM6 signals analysedNo manual reviews · fully automated

Trust Signal Breakdown

high3 sub-signals across 6 dimensions

CVEs, dependency health, and supply chain integrity

3 of 3 sub-signals with data

Known CVEs40%5.0

No known CVEs

via OSV.dev

Dependency Health30%5.0

13 dependencies (minimal)

via npm / PyPI

Supply Chain30%5.0

Supply chain analyzed, no transitive CVEs

via npm provenance

Uptime, latency, error rates, and incident history

0 of 0 sub-signals with data

Commit recency, release cadence, issue response, CI/CD

0 of 0 sub-signals with data

Downloads, stars, dependents, and growth trajectory

0 of 0 sub-signals with data

License, documentation, security policy, changelog

0 of 0 sub-signals with data

Track record, org maturity, community standing

0 of 0 sub-signals with data

About this score

Scored across 3 sub-signals in 6 dimensionsScoring engine v1 (beta) — actively being expandedPhase 1: Core sub-signal architecture (live)Phase 2: Permission scope & expanded collection (in progress)

Trust AssessmentAI Assessment

Optimized gradient boosting library for structured data that delivers high-performance classification, regression, and ranking with GPU acceleration support.

Package Availability (30d)

100.00%

p50: 25ms · p99: 39ms

Avg Latency

30ms

averaged across 30d health checks

Weekly Downloads

—

no package registry data

Incidents & Alertslast 90 days

Mar 1Trust score decreased by 1.383.24

Feb 24Trust score increased by 1.954.47