EvalML vs FEDOT
EvalML
evalml.alteryx.comFEDOT
fedot.readthedocs.ioSide by side
| Figure | EvalML | FEDOT |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · AutoML Software | 7.8 (#13) | 8.7 (#4) |
| Automation | The project README lists automation features including data-quality checks and cross-validation. | Users can choose full automation by omitting parameters or partial automation by supplying parameters for manual composing. |
| Installation | EvalML can be installed from PyPI, conda-forge, or source, with Python 3.9–3.11 supported on the current installation page. | The project README documents installation with pip, including an extra option for image and text processing and deep neural networks. |
| Purpose | EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions. | FEDOT is an open-source framework for automated modeling and machine learning that builds end-to-end solutions using an evolutionary approach. |
| Support | The project directs users to Stack Overflow for usage questions, GitHub issues for bugs and feature requests, Slack for development discussion, and [email protected] for other questions. | The maintainers say they are happy to help users adopt FEDOT to their needs. |
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.