# AxeldeRomblay/MLBox

MLBox is a powerful Automated Machine Learning python library.

Repository: https://github.com/AxeldeRomblay/MLBox
Canonical: https://ross.abutalabs.com/products/mlbox
Homepage: https://mlbox.readthedocs.io/en/latest/
Language: Python
License: NOASSERTION
License Family: other
Topics: machine-learning, auto-ml, kaggle, deep-learning, stacking, pipeline, optimization, preprocessing, encoding, prediction, distributed, xgboost, drift, classification, regression, lightgbm, keras, automated-machine-learning, automl, data-science
Last push: 2023-08-06T18:20:04+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": 3380, "days_push": 1123, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1536, forks 273 (observed 2026-08-28T04:04:59.980631+00:00)

## What it is
MLBox is a Python automated machine learning (AutoML) library that handles data preprocessing, feature selection, leak detection, and hyper-parameter optimization. It provides state-of-the-art predictive models for classification and regression, including deep learning, stacking, LightGBM, and XGBoost, with model interpretation.

## Use cases
- automate machine learning pipelines in python
- automatically tune hyperparameters for classification models
- detect data leakage and select features before training
- build stacked ensemble models for kaggle competitions
- preprocess and clean large datasets with distributed processing
- train regression models with lightgbm and xgboost automatically
- interpret predictions from automated ml models

## When to choose
- you want an end-to-end AutoML pipeline covering preprocessing, feature selection, and model tuning
- you need robust leak detection and feature selection out of the box
- you're competing in kaggle-style tabular ML problems
- you want stacking ensembles with lightgbm, xgboost, and keras models

## When to avoid
- you need a library with frequent updates and active maintenance
- you require deep customization of every pipeline step
- you're working on non-tabular data like images or raw text
- you need a permissively documented, actively developed AutoML alternative

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, etl, data-science
- domain: machine-learning, data-science, artificial-intelligence
- platform: python
- tags: automl, auto-ml, hyperparameter-optimization, feature-selection, stacking, lightgbm, xgboost, drift-detection, classification, regression

## Member repositories
- AxeldeRomblay/MLBox (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:59.980631+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 2026-08-30T04:31:02.878715+00:00, confidence not recorded.
  - readme: https://github.com/AxeldeRomblay/MLBox (fetched 2026-08-28T04:04:59.980631+00:00, sha c0d5487d3512)
  - registry_pypi: https://pypi.org/pypi/mlbox/json (fetched 2026-08-29T11:33:27.759241+00:00, sha f39d7392f573)
- Data as of 2026-08-30T08:39:29.467469+00:00.