---
title: "autokeras vs scikit-learn"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/keras-team-autokeras-vs-scikit-learn-scikit-learn"
tools: ["keras-team-autokeras", "scikit-learn-scikit-learn"]
---

# autokeras vs scikit-learn

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+; pick scikit-learn if use scikit-learn for Python-based machine learning tasks that require robust algorithms, comprehensive documentation, and extensive community support.

[autokeras](http://autokeras.com/) reports 9.3k GitHub stars, 1.4k forks, and 161 open issues, last pushed Nov 25, 2025. [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 [autokeras's repository](https://github.com/keras-team/autokeras) and [scikit-learn's repository](https://github.com/scikit-learn/scikit-learn).

| | [autokeras](/tools/keras-team-autokeras.md) | [scikit-learn](/tools/scikit-learn-scikit-learn.md) |
| --- | --- | --- |
| Tagline | AutoML library for deep learning | machine learning in Python |
| Stars | 9,328 | 66,855 |
| Forks | 1,393 | 27,251 |
| Open issues | 161 | 2,115 |
| Language | Python | Python |
| Adopt for | AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. | Use scikit-learn for Python-based machine learning tasks that require robust algorithms, comprehensive documentation, and extensive community support. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | BSD-3-Clause |
| Categories | Developer Tools, Model Training | Model Training |

## Trust and health

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

| | [autokeras](/tools/keras-team-autokeras.md) | [scikit-learn](/tools/scikit-learn-scikit-learn.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Very active (96%) |
| Days since push | 251d | 1d |
| Open issues (now) | 161 | 2.1k |
| Full report | [trust report](/tools/keras-team-autokeras/trust.md) | [trust report](/tools/scikit-learn-scikit-learn/trust.md) |

## Shared compatibility

- **Python**: [autokeras](/tools/keras-team-autokeras.md) - Python runtime; [scikit-learn](/tools/scikit-learn-scikit-learn.md) - Python runtime

## Decision facts: autokeras

- **Adopt for:** AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.

## 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 autokeras if…

- License: autokeras is Apache-2.0, scikit-learn is BSD-3-Clause.
- Tags unique to autokeras: autodl, automl, deep-learning, keras.
- Also covers Developer Tools.
- When your project involves deep learning tasks requiring minimal manual intervention in designing models.

### Choose scikit-learn if…

- License: scikit-learn is BSD-3-Clause, autokeras is Apache-2.0.
- Tags unique to scikit-learn: data-analysis, data-science, python, statistics.
- When you need a well-documented library with clear examples and strong community support.

## When NOT to use autokeras

- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible.
- If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.

## 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 autokeras and scikit-learn?

autokeras: AutoML library for deep learning. scikit-learn: machine learning in Python. See the comparison table for live GitHub stats and shared categories.

### When should I choose autokeras over scikit-learn?

Choose autokeras over scikit-learn when License: autokeras is Apache-2.0, scikit-learn is BSD-3-Clause; Tags unique to autokeras: autodl, automl, deep-learning, keras; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models.

### When should I choose scikit-learn over autokeras?

Choose scikit-learn over autokeras when License: scikit-learn is BSD-3-Clause, autokeras is Apache-2.0; Tags unique to scikit-learn: data-analysis, data-science, python, statistics; When you need a well-documented library with clear examples and strong community support.

### When should I avoid autokeras?

When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.

### 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 autokeras or scikit-learn more popular on GitHub?

scikit-learn has more GitHub stars (66,855 vs 9,328). Stars measure visibility, not whether either tool fits your constraints.

### Are autokeras and scikit-learn open source?

Yes - both are open-source projects on GitHub (autokeras: Apache-2.0, scikit-learn: BSD-3-Clause).

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

GraphCanon lists graph-backed alternatives at [autokeras alternatives](/tools/keras-team-autokeras/alternatives) and [scikit-learn alternatives](/tools/scikit-learn-scikit-learn/alternatives) ([autokeras markdown twin](/tools/keras-team-autokeras/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/keras-team-autokeras-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, autokeras or scikit-learn?

autokeras: Slowing. 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 autokeras and scikit-learn?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=keras-team-autokeras`](/api/graphcanon/graph?tool=keras-team-autokeras)
- 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/_