ClimbsRocks/auto_ml

Health v2 · maintenance only

Adoption not part of the score

1654 stars · 309 forks observed · 2026-08-28

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

auto_ml is a Python library for automated machine learning that handles feature engineering, model selection, and hyperparameter optimization across scikit-learn, XGBoost, LightGBM, and Keras/TensorFlow models. It is designed to take a DataFrame plus column descriptions and produce a trained, serializable model ready for production predictions.

Use cases

• automate machine learning pipelines in python
• auto train regression and classification models on a dataframe
• automated feature engineering and feature selection
• hyperparameter optimization for xgboost and lightgbm
• serialize a trained model and serve single-row predictions in production
• compare deep learning and gradient boosting models automatically

When to choose

• you want a simple automl wrapper around scikit-learn, XGBoost, and LightGBM
• you need to go from raw DataFrame to a deployable, serializable model quickly
• you want automatic handling of categorical, date, and text columns

When to avoid

• you need an actively maintained library - the repo is explicitly unmaintained
• you need state-of-the-art automl with modern framework support
• you work outside Python or need distributed training

Facets

library · maturity abandoned

machine-learning data-science etl machine-learning data-science artificial-intelligence python automl auto-ml feature-engineering hyperparameter-optimization gradient-boosting xgboost lightgbm scikit-learn deep-learning unmaintained

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