FEDOT

7.0easy start · #5 of 28
in AutoML Software

Free to practise onyes
Free trialnot on record
Well documentedyes
Runs where you workyes

Runs on api, Linux, Mac, self-hosted, Windows.

FEDOT is ranked #5 of 28 in AutoML software on The Geeks Club. It runs on API, Linux, macOS, Self-hosted, Windows. There is a free plan.

FEDOT plans and pricing

All plans

FEDOT Free Open-source AutoML framework · BSD 3-Clause license github.com · 2 Oct 2026

Compared on AutoML software

Feature engineering: Yes
Automated model selection: Yes
Model explainability: Yes
Workflow interface: code
Hosting model: self_hosted

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The record

purpose: FEDOT is an AutoML-like framework for automated generation of data-driven composite models.
supported_tasks: It can solve classification, regression, clustering and forecasting problems.
specific_tasks: The feature documentation lists classification, regression and univariate or multivariate time-series forecasting as supported tasks.
pipeline_optimization: FEDOT uses the open-source GOLEM library for optimization and learning of graph-based pipelines with meta-heuristic methods.
automation: Users can choose full automation by omitting parameters or partial automation by supplying parameters for manual composing.
multimodal_data: FEDOT can work with multimodal data including tables, texts and images.
preprocessing: Its preprocessing handles infinite values, missing values, binary and non-binary categorical features, and extra spaces in categorical data.
model_presets: The framework provides presets including best_quality, fast_train, stable, auto, gpu, ts and automl, with auto as the default.
installation: FEDOT can be installed with pip using `pip install fedot`, with optional image, text-processing and DNN dependencies available through `fedot[extra]`.
cli: Its API can be called from a console without Python code, and predictions are saved as CSV files.
gpu: GPU evaluation uses RAPIDS and currently supports Ridge, Lasso, LogisticRegression, RandomForestClassifier, RandomForestRegressor, KMeans and SVC.
data_inputs: InputData can be created from CSV files, pandas DataFrames, NumPy arrays and time-series CSV data.
validation: The default cross-validation setting is five folds, and users can add metrics to the optimizer to address potential bias.
license: FEDOT is published under the BSD-3 license for use in projects and research.
Price from: Free

Where it ranks on The Geeks Club

Best AutoML Software in 2026#5 of 28

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Sources

fedot.readthedocs.io/en/latest/faq/abstract.html· checked 1 Oct 2026
fedot.readthedocs.io/en/latest/introduction/fedot_features/m· checked 1 Oct 2026
fedot.readthedocs.io/en/latest/introduction/fedot_features/a· checked 1 Oct 2026
fedot.readthedocs.io/en/latest/introduction/tutorial/environ· checked 1 Oct 2026
fedot.readthedocs.io/en/latest/advanced/cli_call.html· checked 1 Oct 2026
fedot.readthedocs.io/en/latest/advanced/gpu_evaluation.html· checked 1 Oct 2026
fedot.readthedocs.io/en/latest/examples/data.html· checked 1 Oct 2026
fedot.readthedocs.io/en/latest/faq/features.html· checked 1 Oct 2026
fedot.readthedocs.io/en/latest/about.html· checked 1 Oct 2026
fedot.readthedocs.io/en/latest/introduction/what_is_fedot.ht· checked 2 Oct 2026
github.com/aimclub/FEDOT· checked 2 Oct 2026
fedot.readthedocs.io/en/latest/introduction/tutorial/quickst· checked 2 Oct 2026