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A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)

This is an end-to-end machine learning project that utilizes LightGBM to predict customer's probability of churning in a bank's credit card service. Identifying likely churners helps plan strategies for retention, maintaining healthy revenue. Acquiring a new customer is costlier than keeping one.

Time Series Forecasting with LightGBM

Practical guide to build end-to-end machine learning pipeline and deploy your model in production,

Parsnip backends for `tree`, `lightGBM` and `Catboost`

2017第一届腾讯社交广告高校算法大赛Rank28_code

High performance gradient boosting for Ruby

Bayesian Optimization and Grid Search for xgboost/lightgbm

An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.

🐳 An all-in-one Docker image for machine learning. Contains all the popular python machine learning librairies (scikit-learn, xgboost, LightGBM, gensim,Keras, etc...).