# sb-ai-lab/LightAutoML

Fast and customizable framework for automatic ML model creation (AutoML)

Repository: https://github.com/sb-ai-lab/LightAutoML
Canonical: https://ross.abutalabs.com/products/lightautoml
Language: Python
License: Apache-2.0
License Family: permissive
Topics: automl, data-science, machine-learning, python, automated-machine-learning, automatic-machine-learning, automl-algorithms, binary-classification, kaggle, lama, multiclass-classification, nlp, regression
Last push: 2026-01-15T12:29:45+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 62, release rhythm 35, longevity 100
- inputs: {"age_days": 1598, "days_push": 230, "days_rel": 272, "gap_med": 173.0, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1472, forks 70 (observed 2026-08-28T04:04:49.428756+00:00)

## What it is
LightAutoML (LAMA) is a Python framework for automatic machine learning model creation (AutoML) supporting tabular, time series, image, and text data. It offers ready-to-use presets that build models in a few lines of code as well as customizable pipeline blocks for advanced users.

## Use cases
- automatically train models on tabular data with a few lines of code
- solve binary classification, multiclass classification, and regression tasks
- build custom AutoML pipelines from reusable blocks
- handle mixed data types including text, images, and time series
- create strong Kaggle competition baselines quickly
- generate interpretable white-box models

## When to choose
- you want automated feature engineering, model selection, and ensembling without manual tuning
- you need fast AutoML for tabular datasets in Python
- you want a customizable AutoML framework with preset and framework modes
- you need support for text and image features alongside tabular data

## When to avoid
- you need full manual control over every modeling step
- you require deep learning architectures beyond what presets provide
- you work outside Python or need distributed large-scale training out of the box

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, nlp
- domain: machine-learning, data-science, artificial-intelligence
- platform: python, cross-platform
- tags: automl, auto-ml, tabular-data, time-series, kaggle, binary-classification, regression, multiclass-classification

## Member repositories
- sb-ai-lab/LightAutoML (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:49.428756+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:34:43.287600+00:00, confidence not recorded.
  - readme: https://github.com/sb-ai-lab/LightAutoML (fetched 2026-08-28T04:04:49.428756+00:00, sha 9b8885986d56)
  - registry_pypi: https://pypi.org/pypi/lightautoml/json (fetched 2026-08-29T11:42:25.094907+00:00, sha cdd63cda2f4f)
- Data as of 2026-08-30T08:39:29.467469+00:00.