# mljar/mljar-supervised

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Repository: https://github.com/mljar/mljar-supervised
Canonical: https://ross.abutalabs.com/products/mljar-supervised
Homepage: https://mljar.com
Language: Python
License: MIT
License Family: permissive
Topics: automl, machine-learning, mljar, data-science, scikit-learn, hyperparameter-optimization, feature-engineering, xgboost, random-forest, neural-network, lightgbm, catboost, ensemble, hyper-parameters, decision-tree, automated-machine-learning, automl-api, automl-python
Last push: 2026-07-27T09:30:27+00:00

## Health v2 (maintenance only)
Score: 91/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 94, release rhythm 83, longevity 100
- inputs: {"age_days": 2858, "days_push": 37, "days_rel": 37, "gap_med": 64, "n_releases_24m": 10}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3287, forks 451 (observed 2026-08-28T04:07:53.757041+00:00)

## What it is
MLJAR AutoML is a Python package for automated machine learning on tabular data, providing feature engineering, hyperparameter tuning, model ensembling, explanations, and automatic documentation. It wraps popular algorithms like XGBoost, LightGBM, CatBoost, random forests, and neural networks behind a simple AutoML API.

## Use cases
- automate machine learning on tabular data
- automatically tune hyperparameters for xgboost and lightgbm
- generate model explanations and documentation
- build ensemble models without manual pipeline coding
- run AutoML for classification and regression in Python
- automatic feature engineering for structured datasets
- compare multiple ML algorithms with one API

## When to choose
- you want a hands-off AutoML pipeline for tabular data with minimal code
- you need built-in feature engineering, tuning, ensembling, and markdown documentation in one tool
- you want explainability (feature importance, SHAP) and fairness-aware training out of the box
- you prefer an MIT-licensed Python library over paid AutoML platforms

## When to avoid
- you work with unstructured data like images, audio, or text-only deep learning
- you need full manual control over every pipeline step rather than automation
- you require a distributed or GPU-cluster-scale AutoML system
- you want a no-code GUI - the core package is a Python library (the GUI is a separate commercial product)

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, benchmarking
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: automl, hyperparameter-optimization, feature-engineering, xgboost, lightgbm, catboost, ensemble-learning, model-explainability, tabular-data, scikit-learn, automation

## Member repositories
- mljar/mljar-supervised (main) score 91

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:53.757041+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 2026-08-30T07:22:54.591307+00:00, confidence not recorded.
  - readme: https://github.com/mljar/mljar-supervised (fetched 2026-08-28T04:07:53.757041+00:00, sha cd748980412e)
  - homepage: https://mljar.com (fetched 2026-08-29T09:36:07.974967+00:00, sha 18157cee3e6c)
  - site_page: https://mljar.com/docs (fetched 2026-08-29T09:36:07.986198+00:00, sha 8f67a34fdd0d)
  - site_page: https://mljar.com/docs/ai-data-analyst (fetched 2026-08-29T09:36:07.988195+00:00, sha 4fd0faa4ef42)
  - site_page: https://mljar.com/docs/autolab-experiments (fetched 2026-08-29T09:36:07.989891+00:00, sha 7c95f1018ef6)
  - site_page: https://mljar.com/docs/supertree (fetched 2026-08-29T09:36:07.991447+00:00, sha c8f9bc8b6923)
  - site_page: https://mljar.com/about (fetched 2026-08-29T09:36:07.994869+00:00, sha e3c2426bbb06)
  - registry_pypi: https://pypi.org/pypi/mljar-supervised/json (fetched 2026-08-29T09:36:07.996359+00:00, sha 7b8fec8809a3)
  - site_page: https://mljar.com/pricing (fetched 2026-08-29T09:36:07.993088+00:00, sha cba119c1478e)
- Data as of 2026-08-30T08:39:29.467469+00:00.