AxeldeRomblay/MLBox

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1536 stars · 273 forks observed · 2026-08-28

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

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

library · maturity maintenance

machine-learning etl data-science machine-learning data-science artificial-intelligence python automl auto-ml hyperparameter-optimization feature-selection stacking lightgbm xgboost drift-detection classification regression

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