Custom Loss Functions and Evaluation Metrics for XGBoost and LightGBM
MIT License
Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]
General boosting framework for any regression estimator
Benchmarking framework for Feature Selection and Feature Ranking algorithms 🚀
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking,...
Differentiable Factor Graph Optimization for Learning Smoothers @ IROS 2021
A library for debugging/inspecting machine learning classifiers and explaining their predictions
A module to compute textual lexical richness (aka lexical diversity).
Bayesian Optimization for Anything: A high-level Bayesian optimization framework and model wrappi...
hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost ...
Easy to use class balanced cross entropy and focal loss implementation for Pytorch