Home - Welcome to MLBox’s official documentation¶
MLBox is a powerful Automated Machine Learning python library. It provides the following features:
- Fast reading and distributed data preprocessing/cleaning/formatting.
- Highly robust feature selection and leak detection.
- Accurate hyper-parameter optimization in high-dimensional space.
- State-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,…).
- Prediction with models interpretation.
Links¶
- Performance experiments:
- Kaggle competition “Two Sigma Connect: Rental Listing Inquiries” (rank: 85/2488)
- Kaggle competition “Sberbank Russian Housing Market” (rank: 190/3274)
- Examples & demos:
- Kaggle kernel on “Titanic” dataset (classification)
- Kaggle kernel on “House Prices” dataset (regression)
- Articles, books & tutorials from users:
- Tutorial on Automated Machine Learning using MLBox (Analytics Vidhya article)
- MLBox: a short regression tutorial (user blog)
- Implementing Auto-ML Systems with Open Source Tools (KDnuggets article)
- Hands-On Automated Machine Learning (O’Reilly book)
- Automatic Machine Learning (Youtube tutorial)
- Automated Machine Learning with MLBox (user blog)
- Introduction to AutoML with MLBox (user blog)
- References: