FEDOT

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About

FEDOT is ranked #3 of 28 in AutoML software on Inferse. It runs on API, Linux, macOS, Self-hosted, Windows. There is a free plan.

Compared on AutoML software

Feature engineering: Yesfedot.readthedocs.io
Automated model selection: Yesfedot.readthedocs.io
Model explainability: Yesfedot.readthedocs.io
Workflow interface: codefedot.readthedocs.io
Hosting model: self_hostedfedot.readthedocs.io

Facts

purpose: FEDOT is an AutoML-like framework for automated generation of data-driven composite models.fedot.readthedocs.io · 1 Oct 2026
supported_tasks: It can solve classification, regression, clustering and forecasting problems.fedot.readthedocs.io · 1 Oct 2026
specific_tasks: The feature documentation lists classification, regression and univariate or multivariate time-series forecasting as supported tasks.fedot.readthedocs.io · 1 Oct 2026
pipeline_optimization: FEDOT uses the open-source GOLEM library for optimization and learning of graph-based pipelines with meta-heuristic methods.fedot.readthedocs.io · 1 Oct 2026
automation: Users can choose full automation by omitting parameters or partial automation by supplying parameters for manual composing.fedot.readthedocs.io · 1 Oct 2026
multimodal_data: FEDOT can work with multimodal data including tables, texts and images.fedot.readthedocs.io · 1 Oct 2026
preprocessing: Its preprocessing handles infinite values, missing values, binary and non-binary categorical features, and extra spaces in categorical data.fedot.readthedocs.io · 1 Oct 2026
model_presets: The framework provides presets including best_quality, fast_train, stable, auto, gpu, ts and automl, with auto as the default.fedot.readthedocs.io · 1 Oct 2026
installation: FEDOT can be installed with pip using `pip install fedot`, with optional image, text-processing and DNN dependencies available through `fedot[extra]`.fedot.readthedocs.io · 1 Oct 2026
cli: Its API can be called from a console without Python code, and predictions are saved as CSV files.fedot.readthedocs.io · 1 Oct 2026
gpu: GPU evaluation uses RAPIDS and currently supports Ridge, Lasso, LogisticRegression, RandomForestClassifier, RandomForestRegressor, KMeans and SVC.fedot.readthedocs.io · 1 Oct 2026
data_inputs: InputData can be created from CSV files, pandas DataFrames, NumPy arrays and time-series CSV data.fedot.readthedocs.io · 1 Oct 2026
validation: The default cross-validation setting is five folds, and users can add metrics to the optimizer to address potential bias.fedot.readthedocs.io · 1 Oct 2026
license: FEDOT is published under the BSD-3 license for use in projects and research.fedot.readthedocs.io · 1 Oct 2026
support: The maintainers say they are happy to help users adopt FEDOT to their needs.fedot.readthedocs.io · 1 Oct 2026
maker: FEDOT is developed and maintained by the NSS Lab, part of the National Center for Cognitive Technologies at ITMO University in Russia.fedot.readthedocs.io · 1 Oct 2026
Purpose: FEDOT is an open-source framework for automated modeling and machine learning that builds end-to-end solutions using an evolutionary approach.fedot.readthedocs.io · 2 Oct 2026
Supported tasks: FEDOT supports binary and multiclass classification, regression, and time-series forecasting.fedot.readthedocs.io · 2 Oct 2026
Data types: FEDOT works with tabular, image, and text data, including multimodal data from more than one source.fedot.readthedocs.io · 2 Oct 2026
ML lifecycle: FEDOT covers preprocessing, model selection, tuning, cross-validation, and serialization.fedot.readthedocs.io · 2 Oct 2026
Pipeline optimization: FEDOT uses the GOLEM library for optimization and learning of graph-based pipelines with meta-heuristic methods.fedot.readthedocs.io · 2 Oct 2026
Automation controls: Users can adjust automation by omitting parameters for full automation or supplying parameters for partial automation.fedot.readthedocs.io · 2 Oct 2026
Model libraries: FEDOT uses models mostly from scikit-learn, statsmodels, and Keras.fedot.readthedocs.io · 2 Oct 2026
Extensibility: The project says FEDOT supports widely used ML libraries such as scikit-learn, CatBoost, and XGBoost, and allows custom libraries to be integrated.github.com · 2 Oct 2026
Preprocessing: FEDOT can replace infinite values, drop rows with many missing values, binarize binary categorical data, and trim extra spaces in categorical features.fedot.readthedocs.io · 2 Oct 2026
Installation: The project README documents installation with pip, including an extra option for image and text processing and deep neural networks.github.com · 2 Oct 2026
Operating systems: The quick-start guide lists Windows, Linux, and macOS as supported operating systems.fedot.readthedocs.io · 2 Oct 2026
Security and license: The project is distributed under the 3-Clause BSD license.github.com · 2 Oct 2026
Maintainer: FEDOT is developed and maintained by the NSS Lab team, part of the National Center for Cognitive Technologies at ITMO University in Russia.fedot.readthedocs.io · 2 Oct 2026
Contributions: The project welcomes contributors to report bugs or propose enhancements through its GitHub issues.fedot.readthedocs.io · 2 Oct 2026