# ClimbsRocks/auto_ml

[UNMAINTAINED] Automated machine learning for analytics & production

Repository: https://github.com/ClimbsRocks/auto_ml
Canonical: https://ross.abutalabs.com/products/auto_ml
Homepage: http://auto-ml.readthedocs.io
Language: Python
License: MIT
License Family: permissive
Topics: machine-learning, data-science, automated-machine-learning, gradient-boosting, scikit-learn, python, machine-learning-pipelines, machine-learning-library, production-ready, automl, lightgbm, analytics, feature-engineering, hyperparameter-optimization, deep-learning, xgboost, keras, deeplearning, tensorflow, artificial-intelligence
Last push: 2021-02-10T07:52:35+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3678, "days_push": 2030, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1654, forks 309 (observed 2026-08-28T04:05:17.220218+00:00)

## What it is
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
- artifact type: library
- maturity: abandoned
- function: machine-learning, data-science, etl
- domain: machine-learning, data-science, artificial-intelligence
- platform: python
- tags: automl, auto-ml, feature-engineering, hyperparameter-optimization, gradient-boosting, xgboost, lightgbm, scikit-learn, deep-learning, unmaintained

## Member repositories
- ClimbsRocks/auto_ml (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:17.220218+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 2026-08-30T03:44:54.349407+00:00, confidence not recorded.
  - readme: https://github.com/ClimbsRocks/auto_ml (fetched 2026-08-28T04:05:17.220218+00:00, sha 986f81fc8e32)
  - registry_pypi: https://pypi.org/pypi/auto_ml/json (fetched 2026-08-29T11:18:19.634222+00:00, sha 57ca37892360)
- Data as of 2026-08-30T08:39:29.467469+00:00.