#Xgboost
Showing 60 of 112 repositories tagged #xgboost, ranked by stars
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
Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现
Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes
Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
A python library for decision tree visualization and model interpretation.
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
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
Deep Learning Server and CLI for Torch and TensorRT
A collection of research papers on decision, classification and regression trees with implementations.
Distributed AI Model Training and LLM Fine-Tuning on Kubernetes
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
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.).
Provide an input CSV and a target field to predict, generate a model + code to run it.
[UNMAINTAINED] Automated machine learning for analytics & production
MLBox is a powerful Automated Machine Learning python library.
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
Scalable machine 🤖 learning for time series forecasting.
Scalable Python DS & ML, in an API compatible & lightning fast way.
An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).
An extension of XGBoost to probabilistic modelling
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.
Impress your boss with interactive Decision Tree visualization
📘 The experiment tracker for foundation model training
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
Fast SHAP value computation for interpreting tree-based models
A unified interface for optimization algorithms and experiments
Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
H2O.ai Machine Learning Interpretability Resources
pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks
Open solution to the Home Credit Default Risk challenge :house_with_garden:
The missing bridge between your ML models and your AI agents.
本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢
Machine learning models for time series analysis
A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment.
Data Science Feature Engineering and Selection Tutorials
State-of-the art Automated Machine Learning python library for Tabular Data
A fast xgboost feature selection algorithm
Machine learning beginner to Kaggle competitor in 30 days. Non-coders welcome. The program starts Monday, August 2, and lasts four weeks. It's designed for people who want to learn machine learning.
基于机器学习的网络安全检测系统 | 集成Kitsune/LUCID算法 | 支持ML/DL/RL模型 | 99.58%攻击检测准确率 | 19913 QPS | Docker/K8s部署
A Streamlit web app that predicts Singapore HDB resale flat prices using a pre-trained XGBoost model. Includes an interactive transaction map, light/dark theme toggle, and Docker support for easy deployment.
Advanced High Performance Data Science Toolbox for R by Laurae
텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)
Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
Tutoriais de Python, Data Science, Machine Learning e Deep Learning - Sigmoidal
AI-powered NBA game outcome predictor that uses advanced team stats and trend-based features to forecast winners and track model performance
Distributed XGBoost on Ray
A container for Deep Learning with Python 3
The objective of this project is to analyze the 3 million grocery orders from more than 200,000 Instacart users and predict which previously purchased item will be in user's next order. Customer segmentation and affinity analysis are done to study customer purchase patterns and for better product marketing and cross-selling.
Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.
A Machine Learning API with native redis caching and export + import using S3. Analyze entire datasets using an API for building, training, testing, analyzing, extracting, importing, and archiving. This repository can run from a docker container or from the repository.
Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision
This repository provides the Open-CE environment files and version definitions for each Open-CE release.
ML/AI training/serving and agent sandbox operator and controller for Kubernetes
🔍🐦🤖 Detect Twitter Bots!
A lightweight gradient boosted decision tree package.
This project studies the intrinsic relationship between the stocks’ multiple factors and the investment value of the stocks listed in China Securities Index 800 Index through the machine method. The investment system pipeline has been implemented including data acquirement, data preprocessing, model tuning and selection based on the XGBoost boosted tree model.