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
Generalized Random Forests
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
الميزات الرئيسية لـ mljar/supertree هي: Decision Tree Models.
تشمل البدائل مفتوحة المصدر لـ mljar/supertree: cerlymarco/linear-tree. dmlc/xgboost — XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for… grf-labs/grf — Generalized Random Forests. h2oai/h2o-3 — h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and… jakecoltman/bartpy. catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,…