FEDOT vs TPOT
FEDOT
fedot.readthedocs.ioTPOT
epistasislab.github.ioSide by side
| Figure | FEDOT | TPOT |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · AutoML Software | 8.7 (#4) | 7.3 (#18) |
| Installation | The project README documents installation with pip, including an extra option for image and text processing and deep neural networks. | TPOT requires Python and can be installed in a conda environment or manually; the documented Python range is >=3.10 and <3.14. |
| license | FEDOT is published under the BSD-3 license for use in projects and research. | TPOT is free software distributed under the GNU Lesser General Public License version 3 or later, without warranty. |
| Preprocessing | FEDOT can replace infinite values, drop rows with many missing values, binarize binary categorical data, and trim extra spaces in categorical features. | With preprocessing enabled, TPOT imputes missing values, one-hot encodes categorical features, and standardizes data. |
| Purpose | FEDOT is an open-source framework for automated modeling and machine learning that builds end-to-end solutions using an evolutionary approach. | TPOT is a Python automated machine learning tool that optimizes machine learning pipelines using genetic programming. |
| support | The maintainers say they are happy to help users adopt FEDOT to their needs. | The project directs users to its GitHub issues to report bugs or suggest enhancements and to discuss extensions. |
Plans
In their own words
FEDOT
FEDOT is a free, open-source framework for automating the creation of data-driven composite models. It supports classification, regression, clustering and time-series forecasting, with tabular, text and image inputs, including multimodal combinations. Its workflow covers preprocessing, model selection, tuning, cross-validation and serialization. Users can leave parameters unspecified for fuller automation or provide them to guide pipeline composition. FEDOT uses the GOLEM library to optimize graph-based pipelines with meta-heuristic methods. Presets include best_quality, fast_train, stable, auto, gpu, ts and automl; auto is the default. Data can come from CSV files, pandas DataFrames, NumPy arrays or time-series CSV data. It can be installed with pip, and the API can be called from a console without Python code, saving predictions as CSV files. GPU evaluation uses RAPIDS and supports a specified set of models. The project is distributed under the BSD 3-Clause license and supports Windows, Linux and macOS.