GraphCanon updated Sep 27, 2026 · GitHub synced Sep 27, 2026
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Decision brief
xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license
Good fit when
- Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.
- Flexible with multi-language support (Python, R, Java) enhancing cross-platform applicability.
Avoid when
- Avoid if ease-of-use and quick model training are more important than fine-tuning or extreme scalability.
- Not suitable when the dataset fits comfortably in memory on a single node, where other simpler tools may exceed.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Sep 27, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 27, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/dmlc/xgboostSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A highly efficient, flexible, and portable gradient boosting library supporting multiple languages including Python, R, Java, Scala, C++, running on various distributed environments.
Capability facts
- Languages
- c++
Source: github.language · Sep 27, 2026
Categories
Tags
README
<img src="https://xgboost.ai/images/logo/xgboost logo trimmed.png" width=200/ eXtreme Gradient Boosting =========== Blog Documentation Resources Contributors Release Notes XGBoost is an optimized distributed gradient boosting library designed to be highly efficient , flexible and...
For agents
This page has a .md twin and JSON over the API.