mljar/mljar-supervised

Health v2 · maintenance only

Adoption not part of the score

3287 stars · 451 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity active

machine-learning data-science benchmarking machine-learning data-science python cross-platform automl hyperparameter-optimization feature-engineering xgboost lightgbm catboost ensemble-learning model-explainability tabular-data scikit-learn automation

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