The tutorial 'CalibratedAdaMEC_ExtendedVersion.ipynb' introduces the concepts of asymmetric (cost-sensitive and/or imbalanced class) learning, decision theory and boosting. It briefly describes the results of the paper:
The main features of nnikolaou/cost-sensitive-boosting-tutorial are: Gradient Boosting Research.
Open-source alternatives to nnikolaou/cost-sensitive-boosting-tutorial include: ajoo/nrgboost — This repository contains the official code of the paper NRGBoost: Energy-Based Generative Boosted Trees (ICLR 2025). amirsaffari/online-multiclass-lpboost — Online Multi-Class LPBoost. andymiller/vboost — code for Variational Boosting: Iteratively Refining Posterior Approximations. anitan0925/resfgb — Remark: The code is updated from the ICML version. The ICML version corresponds to a commit on May 25, 2018. arogozhnikov/infiniteboost — Code for a paper InfiniteBoost: building infinite ensembles with gradient descent (arXiv:1706.01109). A. Rogozhnikov,… aciditeam/acidano.
This repository contains the official code of the paper NRGBoost: Energy-Based Generative Boosted Trees (ICLR 2025).
Online Multi-Class LPBoost
code for Variational Boosting: Iteratively Refining Posterior Approximations
