FEDOT vs MLJAR AutoML

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.

MLJAR AutoML automates supervised machine learning for tabular data, including binary classification, multiclass classification, and regression. Users can work through MLJAR Studio notebooks or use the open-source Python package in scripts and notebooks. Its four modes—Explain, Perform, Compete, and Optuna—have configurable choices for algorithms, validation, metrics, time budget, and explanations. Depending on the selected mode and settings, workflows can preprocess data, select and tune models, engineer and select features, and create ensembles. Experiments include model comparisons, validation results, training times, explanations, and reusable artifacts; summaries are available in Markdown and JSON. Fairness-aware training evaluates models against a chosen metric and threshold and can try sample reweighting, but does not guarantee fairness. The Python package integrates with Mercury for prediction apps supporting individual and batch CSV predictions; apps can be previewed locally, published through MLJAR Platform, or self-hosted with Docker. The package is open source under the MIT license and costs nothing. AutoML is designed for supervised tabular tasks, and reports depend on task and settings.