Top 114 xgboost open source projects

Text classification ai100
Auto viml
Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Advanced High Performance Data Science Toolbox for R by Laurae
Gbm Perf
Performance of various open source GBM implementations
Tensorflow Ml Nlp
텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
Stacked Generalization (Ensemble Learning)
R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.
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.).
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
Machine Learning In R
Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
Automl Gs
Provide an input CSV and a target field to predict, generate a model + code to run it.
Data science blogs
A repository to keep track of all the code that I end up writing for my blog posts.
Outbrain Click Prediction Kaggle
Solution to the Outbrain Click Prediction competition
Nyoka is a Python library to export ML/DL models into PMML (PMML 4.4.1 Standard).
Common library for serving TensorFlow, XGBoost and scikit-learn models in production.
Time Series Forecast with Bitcoin value, to detect upward/down trends with Machine Learning Algorithms
Sci Pype
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.
Dc Hi guides
[Data Castle 算法竞赛] 精品旅行服务成单预测 final rank 11
Fraud Detection Using Machine Learning
Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker
Predicting real estate prices using scikit Learn
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)
Home Credit Default Risk
Default risk prediction for Home Credit competition - Fast, scalable and maintainable SQL-based feature engineering pipeline
Cikm analyticup 2017
CIKM AnalytiCup 2017 is an open competition that is sponsored by Shenzhen Meteorological Bureau, Alibaba Group and CIKM2017. Our team got the third place in the first phrase. And in the second phrase we got the fourth place.
Data Science Competitions
Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).
REST web service for the true real-time scoring (<1 ms) of Scikit-Learn, R and Apache Spark models
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
Ai competitions
A Scala feature transformation library for data science and machine learning
Real time eye tracking for embedded and mobile devices.
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
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
Xgboost Predictor Java
Pure Java implementation of XGBoost predictor for online prediction tasks.
Tiny Gradient Boosting Tree
pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks
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