Comparison
xgboost vs automl-gs
Verdict
Pick xgboost if xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license; pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data.
Markdown twin · xgboost alternatives · automl-gs alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | xgboost | automl-gs |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2w · github_public_v1 | Dormant (2477d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- xgboost
- Scalable, Portable and Distributed Gradient Boosting Library
- automl-gs
- Automatically generate machine-learning models and code with input CSV and target field
Stars
- xgboost
- 29k
- automl-gs
- 1.9k
Forks
- xgboost
- 8.9k
- automl-gs
- 181
Open issues
- xgboost
- 416
- automl-gs
- 28
Language
- xgboost
- C++
- automl-gs
- Python
Adopt for
- xgboost
- xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license
- automl-gs
- automl-gs: Python tool for automated machine-learning model creation from CSV data
Persona
- xgboost
- -
- automl-gs
- -
Runtime
- xgboost
- -
- automl-gs
- -
License
- xgboost
- Apache-2.0 license allows free use, modification and distribution but requires preservation of copyright notices from source files and reproduction of license grants into any copyings of the codebase
- automl-gs
- MIT
Last pushed
- xgboost
- Aug 3, 2026
- automl-gs
- Oct 22, 2019
Categories
- xgboost
- Model Training
- automl-gs
- Data & Retrieval, Model Training
Trust and health
Maintenance
- xgboost
- Very active (96%)
- automl-gs
- Dormant (18%)
Days since push
- xgboost
- 0d
- automl-gs
- 2477d
Open issues (now)
- xgboost
- 416
- automl-gs
- 28
Owner type
- xgboost
- Organization
- automl-gs
- User
OSV dependency advisories
- xgboost
- No lockfile (source not queried)
- automl-gs
- Published findings
Full report
- xgboost
- Trust report
- automl-gs
- Trust report
Choose xgboost if…
- xgboost is primarily C++; automl-gs is Python.
- License: xgboost is Apache-2.0, automl-gs is MIT.
- Tags unique to xgboost: distributed-systems, gbdt, gbm, gbrt.
- Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.
When NOT to use xgboost
- 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.
- Steer clear if your project does not require high-performance gradient boosting models for regression or classification.
Choose automl-gs if…
- automl-gs is primarily Python; xgboost is C++.
- License: automl-gs is MIT, xgboost is Apache-2.0.
- Tags unique to automl-gs: automl, keras, python, tensorflow.
- Also covers Data & Retrieval.
- Need to rapidly prototype models with limited ML expertise
When NOT to use automl-gs
- Complex feature engineering or non-standard data inputs required
- Sensitive about licensing of the generated code
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (dmlc/xgboost) · observed Aug 3, 2026
- GitHub forks (dmlc/xgboost) · observed Aug 3, 2026
- Last push (dmlc/xgboost) · observed Aug 3, 2026
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (minimaxir/automl-gs) · observed Aug 4, 2026
- GitHub forks (minimaxir/automl-gs) · observed Aug 4, 2026
- Last push (minimaxir/automl-gs) · observed Oct 22, 2019
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: xgboost 29k · automl-gs 1.9k (synced Aug 3, 2026).
Common questions
- What is the difference between xgboost and automl-gs?
- xgboost: Scalable, Portable and Distributed Gradient Boosting Library. automl-gs: Automatically generate machine-learning models and code with input CSV and target field. See the comparison table for live GitHub stats and shared categories.
- When should I choose xgboost over automl-gs?
- Choose xgboost over automl-gs when xgboost is primarily C++; automl-gs is Python; License: xgboost is Apache-2.0, automl-gs is MIT; Tags unique to xgboost: distributed-systems, gbdt, gbm, gbrt; Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.
- When should I choose automl-gs over xgboost?
- Choose automl-gs over xgboost when automl-gs is primarily Python; xgboost is C++; License: automl-gs is MIT, xgboost is Apache-2.0; Tags unique to automl-gs: automl, keras, python, tensorflow; Also covers Data & Retrieval; Need to rapidly prototype models with limited ML expertise.
- When should I avoid xgboost?
- 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. Steer clear if your project does not require high-performance gradient boosting models for regression or classification.
- When should I avoid automl-gs?
- Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code
- Is xgboost or automl-gs more popular on GitHub?
- xgboost has more GitHub stars (28,620 vs 1,869). Stars measure visibility, not whether either tool fits your constraints.
- Are xgboost and automl-gs open source?
- Yes - both are open-source projects on GitHub (xgboost: Apache-2.0, automl-gs: MIT).
- Where can I find alternatives to xgboost or automl-gs?
- GraphCanon lists graph-backed alternatives at xgboost alternatives and automl-gs alternatives (xgboost markdown twin, automl-gs markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, xgboost or automl-gs?
- xgboost: Very active. automl-gs: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for xgboost and automl-gs?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: xgboost trust report; automl-gs trust report.