Xgboost
Showing 25 of 25 projects
A scalable, portable, and distributed gradient boosting library for efficient machine learning across multiple languages and platforms.
An open-source, low-code Python library that automates end-to-end machine learning workflows.
A compiler that extends SQL with AI capabilities to train, predict, and evaluate machine learning models directly from SQL statements.
An Automated Machine Learning Python package for tabular data with feature engineering, hyperparameter tuning, explanations, and automatic documentation.
Transpile trained machine learning models into native code (Java, C, Python, Go, etc.) with zero dependencies.
An open-source deep learning API and server written in C++ that supports multiple backends like PyTorch, TensorRT, and TensorFlow for training and inference.
A curated collection of research papers on decision, classification, and regression trees with implementations from top ML conferences.
Automatically visualize any dataset with a single line of code, including data quality assessment and fixes.
A minimal benchmark comparing scalability, speed, and accuracy of popular open-source machine learning libraries for binary classification.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A Python framework for scalable time series forecasting using machine learning models, designed for production environments.
A curated collection of gradient boosting research papers with implementations from top machine learning conferences.
An automated feature generation framework for tabular data that discovers expert-level features to boost machine learning model performance.
Automatically builds high-performance interpretable machine learning models with minimal features using a single line of code.
A pure Go library for making predictions with Gradient Boosting Regression Trees models from LightGBM, XGBoost, and scikit-learn.
An open-source machine learning solution for the Home Credit Default Risk Kaggle competition, providing reproducible code and experiments.
A Julia interface for XGBoost, providing efficient distributed gradient boosting for regression, classification, and ranking.
A fast feature selection algorithm for tree-based models like XGBoost, designed to outperform Boruta in speed and performance.
A Python library for stacked generalization (ensemble learning) that supports scikit-learn, XGBoost, and Keras models with out-of-fold prediction saving.
A Node.js library implementing Decision Tree (ID3/CART), Random Forest, and XGBoost algorithms with TypeScript support and automatic data type detection.
A Ruby interface to XGBoost, providing high-performance gradient boosting for machine learning tasks.
A Python library providing SigOpt hyperparameter optimization wrappers for scikit-learn and XGBoost models.
Provides SigOpt wrappers for scikit-learn to optimize hyperparameters and facilitate model selection.
Related Tags
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.