#xgboost (16 Repositories)
Ranked open-source repositories tagged with #xgboost, scored by pull request acceptance likelihood and maintainer engagement velocity.
20.5%
28.7h
16 repositories tagged #xgboost
Nixtla/mlforecast
Scalable machine 🤖 learning for time series forecasting.
dmlc/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
kserve/kserve
Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes
StatMixedML/XGBoostLSS
An extension of XGBoost to probabilistic modelling
jolibrain/deepdetect
Deep Learning Server and CLI for Torch and TensorRT
dmitryikh/leaves
pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks
zimingttkx/Network-Security-Based-On-ML
NIPS — 基于机器学习的网络入侵防御系统 | 集成Kitsune/LUCID算法 | 在线无监督异常检测 | 内核级实时流量拦截 | FastAPI + CLI 管理
Laurae2/Laurae
Advanced High Performance Data Science Toolbox for R by Laurae
komiya-atsushi/xgboost-predictor-java
Pure Java implementation of XGBoost predictor for online prediction tasks.
wepe/tgboost
Tiny Gradient Boosting Tree
AutoViML/AutoViz
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
BayesWitnesses/m2cgen
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
minimaxir/automl-gs
Provide an input CSV and a target field to predict, generate a model + code to run it.
spotify/featran
A Scala feature transformation library for data science and machine learning
alibaba/Alink
Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.
szilard/benchm-ml
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).