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Automated Machine Learning library for fast, robust model pipelines
MLBox streamlines end‑to‑end AutoML with distributed preprocessing, advanced feature selection, high‑dimensional hyper‑parameter tuning, and state‑of‑the‑art models, delivering interpretable predictions for classification and regression.
MLBox is a Python library that automates the full machine‑learning workflow, from raw data ingestion to model interpretation. It targets data scientists, ML engineers, and researchers who need a reproducible pipeline without hand‑crafting each step.
The library reads large datasets quickly and can distribute preprocessing tasks such as cleaning, encoding, and formatting across multiple cores or nodes. Its feature‑selection module automatically detects data leaks and selects the most predictive variables. Hyper‑parameter optimization explores high‑dimensional search spaces efficiently, while a collection of state‑of‑the‑art algorithms—including deep‑learning networks, LightGBM, XGBoost, and stacking ensembles—covers both classification and regression problems. After training, MLBox provides built‑in interpretation tools that surface feature importance and other explanatory metrics.
MLBox is distributed via PyPI and can be installed with a single pip install mlbox command, making integration into existing Python environments straightforward.
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MLBox supports the Python versions indicated by the PyPI badge, covering recent Python 3 releases.
Install via pip with `pip install mlbox` and import the desired modules in your script.
Yes, it includes tools to generate feature importance and other interpretability metrics for trained models.
The preprocessing components are designed for distributed execution, allowing scaling across multiple cores or machines.
The library is released under the BSD‑3‑Clause license, permitting free commercial and non‑commercial use.
Project at a glance
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