{"data":{"slug":"aimclub-fedot","name":"FEDOT","tagline":"Automated modeling and machine learning framework FEDOT","github_url":"https://github.com/aimclub/FEDOT","owner":"aimclub","repo":"FEDOT","owner_avatar_url":"https://avatars.githubusercontent.com/u/65946329?v=4","primary_language":"Python","stars":709,"forks":92,"topics":["automated-machine-learning","automation","automl","evolutionary-algorithms","fedot","genetic-programming","hyperparameter-optimization","machine-learning","multimodality","parameter-tuning","structural-learning"],"archived":false,"github_pushed_at":"2026-07-08T16:23:47+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/aimclub-fedot","markdown_url":"https://www.graphcanon.com/tools/aimclub-fedot.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/aimclub-fedot","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=aimclub-fedot","description":"Automated modeling and machine learning framework FEDOT","homepage_url":"https://fedot.readthedocs.io","license":"BSD-3-Clause","open_issues":83,"watchers":9,"ai_summary":null,"readme_excerpt":".. |eng| image:: https://img.shields.io/badge/lang-en-red.svg\n   :target: /README_en.rst\n\n.. |rus| image:: https://img.shields.io/badge/lang-ru-yellow.svg\n   :target: /README.rst\n\n.. image:: /docs/fedot_logo.png\n   :alt: Logo of FEDOT framework\n\n.. start-badges\n.. list-table::\n   :stub-columns: 1\n\n   * - package\n     - | |pypi| |python|\n   * - tests\n     - | |build| |integration| |coverage|\n   * - docs\n     - |docs|\n   * - license\n     - | |license|\n   * - stats\n     - | |downloads_stats|\n   * - support\n     - | |tg|\n   * - languages\n     - | |eng| |rus|\n   * - mirror\n     - | |gitlab|\n   * - funding\n     - | |ITMO| |NCCR|\n.. end-badges\n\n**FEDOT** is an open-source framework for automated modeling and machine learning (AutoML) problems. This framework is distributed under the 3-Clause BSD license.\n\nIt provides automatic generative design of machine learning pipelines for various real-world problems. The core of FEDOT is based on an evolutionary approach and supports classification (binary and multiclass), regression, clustering, and time series prediction problems.\n\n.. image:: /docs/fedot-workflow.png\n   :alt: The structure of the AutoML workflow in FEDOT\n\nThe key feature of the framework is the complex management of interactions between various blocks of pipelines. It is represented as a graph that defines connections between data preprocessing and model blocks.\n\nThe project is maintained by the research team of the Natural Systems Simulation Lab, which is a part of the `National Center for Cognitive Research of ITMO University <https://actcognitive.org/>`__.\n\nMore details about FEDOT are available in the next video:\n\n\n.. image:: https://res.cloudinary.com/marcomontalbano/image/upload/v1606396758/video_to_markdown/images/youtube--RjbuV6i6de4-c05b58ac6eb4c4700831b2b3070cd403.jpg\n   :target: http://www.youtube.com/watch?v=RjbuV6i6de4\n   :alt: Introducing Fedot\n\nFEDOT concepts\n==============\n\n- **Flexibility.** FEDOT can be used to automate the construction of solutions for various `problems <https://fedot.readthedocs.io/en/master/introduction/fedot_features/main_features.html#involved-tasks>`_, `data types <https://fedot.readthedocs.io/en/master/introduction/fedot_features/automation_features.html#data-nature>`_ (texts, images, tables), and `models <https://fedot.readthedocs.io/en/master/advanced/automated_pipelines_design.html>`_;\n- **Extensibility.** Pipeline optimization algorithms are data- and task-independent, yet you can use `special strategies <https://fedot.readthedocs.io/en/master/api/strategies.html>`_ for specific tasks or data types (time-series forecasting, NLP, tabular data, etc.) to increase the efficiency;\n- **Integrability.** FEDOT supports widely used ML libraries (Scikit-learn, CatBoost, XGBoost, etc.) and allows you to integrate `custom ones <https://fedot.readthedocs.io/en/master/api/strategies.html#module-fedot.core.operations.evaluation.custom>`_;\n- **Tuningability.** Various `hyper-parameters tuning methods <https://fedot.readthedocs.io/en/master/advanced/hyperparameters_tuning.html>`_ are supported including models' custom evaluation metrics and search spaces;\n- **Versatility.** FEDOT is `not limited to specific modeling tasks <https://fedot.readthedocs.io/en/master/advanced/architecture.html>`_, for example, it can be used in ODE or PDE;\n- **Reproducibility.** Resulting pipelines can be `exported separately as JSON <https://fedot.readthedocs.io/en/master/advanced/pipeline_import_export.html>`_ or `together with your input data as ZIP archive <https://fedot.readthedocs.io/en/master/advanced/project_import_export.html>`_ for experiments reproducibility;\n- **Customizability.** FEDOT allows `managing models complexity <https://fedot.readthedocs.io/en/master/introduction/fedot_features/automation_features.html#models-used>`_ and thereby achieving desired quality.\n\nInstallation\n============\n\n- Package installer for Python **pip**\n\nThe simplest way to install FEDOT is using ``pip``:\n\n.. code-block::\n\n  $ pip","github_created_at":"2020-01-13T12:48:37+00:00","created_at":"2026-07-11T23:34:02.161235+00:00","updated_at":"2026-07-11T23:34:13.144185+00:00","categories":[{"slug":"data-retrieval","name":"Data 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