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Natural Gradient Boosting for Probabilistic Prediction
The main features of stanfordmlgroup/ngboost are: Gradient Boosting, Gradient Boosting Research.
Projects with overlapping indexed features include: dmlc/xgboost — XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for… catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,… amirsaffari/online-multiclass-lpboost — Online Multi-Class LPBoost. 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,… andymiller/vboost — code for Variational Boosting: Iteratively Refining Posterior Approximations.
XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for regression, classification, and ranking. It functions as a predictive model framework and a cross-language toolkit, providing a core implementation with native bindings for Python, R, Java, Scala, and C++. The system is designed as a GPU-accelerated library that utilizes CUDA and NCCL to speed up the training of decision tree ensembles. It operates as a distributed framework capable of scaling training and prediction across multi-node clusters and GPU environments to process m
CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu
Online Multi-Class LPBoost
code for Variational Boosting: Iteratively Refining Posterior Approximations