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LightAutoML
What it is
LightAutoML is an open-source Python library for automated machine learning on tabular and text data. It supports binary and multiclass classification as well as regression, with ready-made tabular, text, and WhiteBox presets or modular custom pipelines. Pipeline creation can automate data processing and typing, feature selection, hyperparameter tuning, time utilization, and report creation. Documented model classes include linear models, LightGBM and CatBoost boosted trees, neural networks, and a WhiteBox scorecard model. Its basic tutorial describes automatic handling of missing values and outliers. Optional installation extras cover NLP, computer vision, and reports, and a tutorial demonstrates a SQL data source. The library is installed from PyPI with `pip install lightautoml` and is licensed under Apache License, Version 2.0. The current package supports datasets with independent samples in each row; multitable datasets and sequences are still a work in progress. The project is intended to help data scientists and analysts reduce routine preparation and model-selection work.
For and against
For
- Supports classification and regression tasks.
- Offers ready-made presets and custom pipelines.
- Can automate tuning and feature selection.
- Tutorial describes handling missing values and outliers.
- Apache License, Version 2.0.
Against
- Multitable datasets are still in progress.
- Sequence support is still in progress.
- Requires installation as a Python library.
The verdict
LightAutoML brings several automated pipeline steps and model options to Python-based tabular and text workflows. Check whether its current row-based dataset support matches your data before adopting it.
Who it suits
LightAutoML is aimed at data scientists and analysts working with tabular or text data who want to automate parts of pipeline creation. It may fit projects using independent samples per row; multitable datasets and sequences remain in progress.
Plans and prices
LightAutoML
Open-source Python library · installable from PyPI · Apache License 2.0
Read 29 Sep 2026 · github.com
On the record
- Automation
- Its pipeline creation supports automatic hyperparameter tuning, data processing, typing, feature selection, time utilization, and report creation.lightautoml.readthedocs.io · read 29 Sep 2026
- Data handling
- The basic tutorial says LightAutoML can handle missing values and outliers automatically.lightautoml.readthedocs.io · read 29 Sep 2026
- Dataset limits
- The repository says the current package handles datasets with independent samples in each row, while multitable datasets and sequences are a work in progress.github.com · read 29 Sep 2026
- Installation
- The documentation says to install LightAutoML from PyPI with `pip install lightautoml`.lightautoml.readthedocs.io · read 29 Sep 2026
- Integrations
- The project offers optional installation extras for NLP, computer vision, and reports, and its tutorial demonstrates a SQL data source.github.com · read 29 Sep 2026
- Intended users
- The maker describes LightAutoML as a framework created by Sber AI Lab to help data scientists and analysts reduce routine data preparation and model selection work.developers.sber.ru · read 29 Sep 2026
- License
- The repository states that the project is licensed under Apache License, Version 2.0.github.com · read 29 Sep 2026
- Models
- Documented model classes include linear models, LightGBM and CatBoost boosted trees, neural networks, and a WhiteBox scorecard model.lightautoml.readthedocs.io · read 29 Sep 2026
- Pipeline options
- The documentation describes ready-made tabular, text, and WhiteBox presets, plus modular custom pipeline creation.lightautoml.readthedocs.io · read 29 Sep 2026
- Purpose
- LightAutoML is an open-source Python library for automated machine learning on tabular and text data.lightautoml.readthedocs.io · read 29 Sep 2026
- Security
- The opened project documentation and repository pages provide no security or compliance claims.github.com · read 29 Sep 2026
- Support
- The repository directs users to its Slack community or Telegram group for advice and GitHub issues for bug reports and feature requests.github.com · read 29 Sep 2026
- Tasks
- The project README lists binary classification, multiclass classification, and regression as supported model creation tasks.github.com · read 29 Sep 2026
