---
title: "automl-gs vs scikit-learn"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/minimaxir-automl-gs-vs-scikit-learn-scikit-learn"
tools: ["minimaxir-automl-gs", "scikit-learn-scikit-learn"]
---

# automl-gs vs scikit-learn

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data; pick scikit-learn if use scikit-learn for Python-based machine learning tasks that require robust algorithms, comprehensive documentation, and extensive community support.

[automl-gs](https://github.com/minimaxir/automl-gs) reports 1.9k GitHub stars, 181 forks, and 28 open issues, last pushed Oct 22, 2019. [scikit-learn](https://scikit-learn.org) has 67k stars, 27k forks, and 2.1k open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [automl-gs's repository](https://github.com/minimaxir/automl-gs) and [scikit-learn's repository](https://github.com/scikit-learn/scikit-learn).

| | [automl-gs](/tools/minimaxir-automl-gs.md) | [scikit-learn](/tools/scikit-learn-scikit-learn.md) |
| --- | --- | --- |
| Tagline | Automatically generate machine-learning models and code with input CSV and target field | machine learning in Python |
| Stars | 1,869 | 66,855 |
| Forks | 181 | 27,251 |
| Open issues | 28 | 2,115 |
| Language | Python | Python |
| Adopt for | automl-gs: Python tool for automated machine-learning model creation from CSV data | Use scikit-learn for Python-based machine learning tasks that require robust algorithms, comprehensive documentation, and extensive community support. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | BSD-3-Clause |
| Categories | Data & Retrieval, Model Training | Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [automl-gs](/tools/minimaxir-automl-gs.md) | [scikit-learn](/tools/scikit-learn-scikit-learn.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 2477d | 1d |
| Open issues (now) | 28 | 2.1k |
| Owner type | User | Organization |
| Full report | [trust report](/tools/minimaxir-automl-gs/trust.md) | [trust report](/tools/scikit-learn-scikit-learn/trust.md) |

## Decision facts: automl-gs

- **Adopt for:** automl-gs: Python tool for automated machine-learning model creation from CSV data

## Decision facts: scikit-learn

- **Adopt for:** Use scikit-learn for Python-based machine learning tasks that require robust algorithms, comprehensive documentation, and extensive community support.

## Choose when

### Choose automl-gs if…

- License: automl-gs is MIT, scikit-learn is BSD-3-Clause.
- Tags unique to automl-gs: automl, keras, tensorflow, xgboost.
- Also covers Data & Retrieval.
- Need to rapidly prototype models with limited ML expertise

### Choose scikit-learn if…

- License: scikit-learn is BSD-3-Clause, automl-gs is MIT.
- Tags unique to scikit-learn: data-analysis, data-science, statistics.
- When you need a well-documented library with clear examples and strong community support.

## When NOT to use automl-gs

- Complex feature engineering or non-standard data inputs required
- Sensitive about licensing of the generated code

## When NOT to use scikit-learn

- Avoid if you require cutting-edge deep learning capabilities or model training that is more efficiently managed with GPU accelerators.
- Not ideal when dealing with very large datasets that benefit from out-of-core computation, as it lacks native support for such functionalities.
- If real-time machine learning predictions are critical and need ultra-low latency, other tools might offer better performance.

## Common questions

### What is the difference between automl-gs and scikit-learn?

automl-gs: Automatically generate machine-learning models and code with input CSV and target field. scikit-learn: machine learning in Python. See the comparison table for live GitHub stats and shared categories.

### When should I choose automl-gs over scikit-learn?

Choose automl-gs over scikit-learn when License: automl-gs is MIT, scikit-learn is BSD-3-Clause; Tags unique to automl-gs: automl, keras, tensorflow, xgboost; Also covers Data & Retrieval; Need to rapidly prototype models with limited ML expertise.

### When should I choose scikit-learn over automl-gs?

Choose scikit-learn over automl-gs when License: scikit-learn is BSD-3-Clause, automl-gs is MIT; Tags unique to scikit-learn: data-analysis, data-science, statistics; When you need a well-documented library with clear examples and strong community support.

### When should I avoid automl-gs?

Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code

### When should I avoid scikit-learn?

Avoid if you require cutting-edge deep learning capabilities or model training that is more efficiently managed with GPU accelerators. Not ideal when dealing with very large datasets that benefit from out-of-core computation, as it lacks native support for such functionalities. If real-time machine learning predictions are critical and need ultra-low latency, other tools might offer better performance.

### Is automl-gs or scikit-learn more popular on GitHub?

scikit-learn has more GitHub stars (66,855 vs 1,869). Stars measure visibility, not whether either tool fits your constraints.

### Are automl-gs and scikit-learn open source?

Yes - both are open-source projects on GitHub (automl-gs: MIT, scikit-learn: BSD-3-Clause).

### Where can I find alternatives to automl-gs or scikit-learn?

GraphCanon lists graph-backed alternatives at [automl-gs alternatives](/tools/minimaxir-automl-gs/alternatives) and [scikit-learn alternatives](/tools/scikit-learn-scikit-learn/alternatives) ([automl-gs markdown twin](/tools/minimaxir-automl-gs/alternatives.md), [scikit-learn markdown twin](/tools/scikit-learn-scikit-learn/alternatives.md)), 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](/compare/minimaxir-automl-gs-vs-scikit-learn-scikit-learn.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, automl-gs or scikit-learn?

automl-gs: Dormant. scikit-learn: Very active. 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 automl-gs and scikit-learn?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [automl-gs trust report](/tools/minimaxir-automl-gs/trust); [scikit-learn trust report](/tools/scikit-learn-scikit-learn/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=minimaxir-automl-gs`](/api/graphcanon/graph?tool=minimaxir-automl-gs)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_