--- title: "automl-gs vs metric-learn" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/minimaxir-automl-gs-vs-scikit-learn-contrib-metric-learn" tools: ["minimaxir-automl-gs", "scikit-learn-contrib-metric-learn"] --- # automl-gs vs metric-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 metric-learn if metric-learn is a Python library for metric learning that offers a range of algorithms compatible with scikit-learn's API and supports various methods like LMNN, ITML, LFDA among others. [automl-gs](https://github.com/minimaxir/automl-gs) reports 1.9k GitHub stars, 181 forks, and 28 open issues, last pushed Oct 22, 2019. [metric-learn](http://contrib.scikit-learn.org/metric-learn/) has 1.4k stars, 231 forks, and 51 open issues, last pushed Mar 19, 2026. Figures are from public GitHub metadata via [automl-gs's repository](https://github.com/minimaxir/automl-gs) and [metric-learn's repository](https://github.com/scikit-learn-contrib/metric-learn). | | [automl-gs](/tools/minimaxir-automl-gs.md) | [metric-learn](/tools/scikit-learn-contrib-metric-learn.md) | | --- | --- | --- | | Tagline | Automatically generate machine-learning models and code with input CSV and target field | Metric learning algorithms in Python | | Stars | 1,869 | 1,438 | | Forks | 181 | 231 | | Open issues | 28 | 51 | | Language | Python | Python | | Adopt for | automl-gs: Python tool for automated machine-learning model creation from CSV data | Metric-learn is a Python library for metric learning that offers a range of algorithms compatible with scikit-learn's API and supports various methods like LMNN, ITML, LFDA among others. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | 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) | [metric-learn](/tools/scikit-learn-contrib-metric-learn.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 2477d | 136d | | Open issues (now) | 28 | 51 | | Owner type | User | Organization | | Full report | [trust report](/tools/minimaxir-automl-gs/trust.md) | [trust report](/tools/scikit-learn-contrib-metric-learn/trust.md) | ## Decision facts: automl-gs - **Adopt for:** automl-gs: Python tool for automated machine-learning model creation from CSV data ## Decision facts: metric-learn - **Requirements:** The application requires Python version 3.6 or higher and specific dependencies such as numpy, scipy, and scikit-learn. - **Adopt for:** Metric-learn is a Python library for metric learning that offers a range of algorithms compatible with scikit-learn's API and supports various methods like LMNN, ITML, LFDA among others. ## Choose when ### Choose automl-gs if… - Tags unique to automl-gs: automl, keras, tensorflow, xgboost. - Also covers Data & Retrieval. - Need to rapidly prototype models with limited ML expertise ### Choose metric-learn if… - Requirements: The application requires Python version 3.6 or higher and specific dependencies such as numpy, scipy, and scikit-learn.. - Tags unique to metric-learn: metric-learning, scikit-learn. - When you need to use specific metric learning techniques such as Large Margin Nearest Neighbor (LMNN) or Neighborhood Components Analysis (NCA), which are implemented efficiently in Python. ## 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 metric-learn - If your development environment does not already use Python, as metric-learn is specific to this language and its ecosystem. - For applications that require real-time performance critical operations, since the library may rely on computationally intensive algorithms that could affect latency in real-time systems. ## Common questions ### What is the difference between automl-gs and metric-learn? automl-gs: Automatically generate machine-learning models and code with input CSV and target field. metric-learn: Metric learning algorithms in Python. See the comparison table for live GitHub stats and shared categories. ### When should I choose automl-gs over metric-learn? Choose automl-gs over metric-learn when 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 metric-learn over automl-gs? Choose metric-learn over automl-gs when Requirements: The application requires Python version 3.6 or higher and specific dependencies such as numpy, scipy, and scikit-learn.; Tags unique to metric-learn: metric-learning, scikit-learn; When you need to use specific metric learning techniques such as Large Margin Nearest Neighbor (LMNN) or Neighborhood Components Analysis (NCA), which are implemented efficiently in Python. ### 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 metric-learn? If your development environment does not already use Python, as metric-learn is specific to this language and its ecosystem. For applications that require real-time performance critical operations, since the library may rely on computationally intensive algorithms that could affect latency in real-time systems. ### Is automl-gs or metric-learn more popular on GitHub? automl-gs has more GitHub stars (1,869 vs 1,438). Stars measure visibility, not whether either tool fits your constraints. ### Are automl-gs and metric-learn open source? Yes - both are open-source projects on GitHub (automl-gs: MIT, metric-learn: MIT). ### Where can I find alternatives to automl-gs or metric-learn? GraphCanon lists graph-backed alternatives at [automl-gs alternatives](/tools/minimaxir-automl-gs/alternatives) and [metric-learn alternatives](/tools/scikit-learn-contrib-metric-learn/alternatives) ([automl-gs markdown twin](/tools/minimaxir-automl-gs/alternatives.md), [metric-learn markdown twin](/tools/scikit-learn-contrib-metric-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-contrib-metric-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 metric-learn? automl-gs: Dormant. metric-learn: Slowing. 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 metric-learn? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [automl-gs trust report](/tools/minimaxir-automl-gs/trust); [metric-learn trust report](/tools/scikit-learn-contrib-metric-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/_