xgboost-details.zip_xgboost_xgboost源码_机器学习

Awesome XGBoost =============== This page contains a curated list of examples, tutorials, blogs about XGBoost usecases. It is inspired by [awesome-MXNet](https://github.com/dmlc/mxnet/blob/master/example/README.md), [awesome-php](https://github.com/ziadoz/awesome-php) and [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning). Please send a pull request if you find things that belongs to here. Contents -------- - [Code Examples](#code-examples) - [Features Walkthrough](#features-walkthrough) - [Basic Examples by Tasks](#basic-examples-by-tasks) - [Benchmarks](#benchmarks) - [Machine Learning Challenge Winning Solutions](#machine-learning-challenge-winning-solutions) - [Tutorials](#tutorials) - [Usecases](#usecases) - [Tools using XGBoost](#tools-using-xgboost) - [Awards](#awards) - [Windows Binaries](#windows-binaries) Code Examples ------------- ### Features Walkthrough This is a list of short codes introducing different functionalities of xgboost packages. * Basic walkthrough of packages [python](guide-python/basic_walkthrough.py) [R](../R-package/demo/basic_walkthrough.R) [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl) [PHP](https://github.com/bpachev/xgboost-php/blob/master/demo/titanic_demo.php) * Customize loss function, and evaluation metric [python](guide-python/custom_objective.py) [R](../R-package/demo/custom_objective.R) [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/custom_objective.jl) * Boosting from existing prediction [python](guide-python/boost_from_prediction.py) [R](../R-package/demo/boost_from_prediction.R) [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl) * Predicting using first n trees [python](guide-python/predict_first_ntree.py) [R](../R-package/demo/predict_first_ntree.R) [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/predict_first_ntree.jl) * Generalized Linear Model [python](guide-python/generalized_linear_model.py) [R](../R-package/demo/generalized_linear_model.R) [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/generalized_linear_model.jl) * Cross validation [python](guide-python/cross_validation.py) [R](../R-package/demo/cross_validation.R) [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/cross_validation.jl) * Predicting leaf indices [python](guide-python/predict_leaf_indices.py) [R](../R-package/demo/predict_leaf_indices.R) ### Basic Examples by Tasks Most of examples in this section are based on CLI or python version. However, the parameter settings can be applied to all versions - [Binary classification](binary_classification) - [Multiclass classification](multiclass_classification) - [Regression](regression) - [Learning to Rank](rank) ### Benchmarks - [Starter script for Kaggle Higgs Boson](kaggle-higgs) - [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution) - [Benchmarking the most commonly used open source tools for binary classification](https://github.com/szilard/benchm-ml#boosting-gradient-boosted-treesgradient-boosting-machines) ## Machine Learning Challenge Winning Solutions XGBoost is extensively used by machine learning practitioners to create state of art data science solutions, this is a list of machine learning winning solutions with XGBoost. Please send pull requests if you find ones that are missing here. - Maksims Volkovs, Guangwei Yu and Tomi Poutanen, 1st place of the [2017 ACM RecSys challenge](http://2017.recsyschallenge.com/). Link to [paper](http://www.cs.toronto.edu/~mvolkovs/recsys2017_challenge.pdf). - Vlad Sandulescu, Mihai Chiru, 1st place of the [KDD Cup 2016 competition](https://kddcup2016.azurewebsites.net). Link to [the arxiv paper](http://arxiv.org/abs/1609.02728). - Marios Michailidis, Mathias Müller and HJ van Veen, 1st place of the [Dato Truely Native? competition](https://www.kaggle.com/c/dato-native). Link to [the Kaggle interview](http://blog.kaggle.com/2015/12/03/dato-winners-interview-1st-place-mad-professors/). - Vlad Mironov, Alexander Guschin, 1st place of the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Link to [the Kaggle interview](http://blog.kaggle.com/2015/11/30/flavour-of-physics-technical-write-up-1st-place-go-polar-bears/). - Josef Slavicek, 3rd place of the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Link to [the Kaggle interview](http://blog.kaggle.com/2015/11/23/flavour-of-physics-winners-interview-3rd-place-josef-slavicek/). - Mario Filho, Josef Feigl, Lucas, Gilberto, 1st place of the [Caterpillar Tube Pricing competition](https://www.kaggle.com/c/caterpillar-tube-pricing). Link to [the Kaggle interview](http://blog.kaggle.com/2015/09/22/caterpillar-winners-interview-1st-place-gilberto-josef-leustagos-mario/). - Qingchen Wang, 1st place of the [Liberty Mutual Property Inspection](https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction). Link to [the Kaggle interview](http://blog.kaggle.com/2015/09/28/liberty-mutual-property-inspection-winners-interview-qingchen-wang/). - Chenglong Chen, 1st place of the [Crowdflower Search Results Relevance](https://www.kaggle.com/c/crowdflower-search-relevance). Link to [the winning solution](https://www.kaggle.com/c/crowdflower-search-relevance/forums/t/15186/1st-place-winner-solution-chenglong-chen/). - Alexandre Barachant (“Cat”) and Rafał Cycoń (“Dog”), 1st place of the [Grasp-and-Lift EEG Detection](https://www.kaggle.com/c/grasp-and-lift-eeg-detection). Link to [the Kaggle interview](http://blog.kaggle.com/2015/10/12/grasp-and-lift-eeg-winners-interview-1st-place-cat-dog/). - Halla Yang, 2nd place of the [Recruit Coupon Purchase Prediction Challenge](https://www.kaggle.com/c/coupon-purchase-prediction). Link to [the Kaggle interview](http://blog.kaggle.com/2015/10/21/recruit-coupon-purchase-winners-interview-2nd-place-halla-yang/). - Owen Zhang, 1st place of the [Avito Context Ad Clicks competition](https://www.kaggle.com/c/avito-context-ad-clicks). Link to [the Kaggle interview](http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/). - Keiichi Kuroyanagi, 2nd place of the [Airbnb New User Bookings](https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings). Link to [the Kaggle interview](http://blog.kaggle.com/2016/03/17/airbnb-new-user-bookings-winners-interview-2nd-place-keiichi-kuroyanagi-keiku/). - Marios Michailidis, Mathias Müller and Ning Situ, 1st place [Homesite Quote Conversion](https://www.kaggle.com/c/homesite-quote-conversion). Link to [the Kaggle interview](http://blog.kaggle.com/2016/04/08/homesite-quote-conversion-winners-write-up-1st-place-kazanova-faron-clobber/). ## Talks - [XGBoost: A Scalable Tree Boosting System](http://datascience.la/xgboost-workshop-and-meetup-talk-with-tianqi-chen/) (video+slides) by Tianqi Chen at the Los Angeles Data Science meetup ## Tutorials - [Machine Learning with XGBoost on Qubole Spark Cluster](https://www.qubole.com/blog/machine-learning-xgboost-qubole-spark-cluster/) - [XGBoost Official RMarkdown Tutorials](https://xgboost.readthedocs.org/en/latest/R-package/index.html#tutorials) - [An Introduction to XGBoost R Package](http://dmlc.ml/rstats/2016/03/10/xgboost.html) by Tong He - [Open Source Tools & Data Science Competitions](http://www.slideshare.net/odsc/owen-zhangopen-sourcetoolsanddscompetitions1) by Owen Zhang - XGBoost parameter tuning tips * [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit) * [Winning solution of Kaggle Higgs competition: what a single model can do](http://no2147483647.wordpress.com/2014/09/17/winning-solution-of-kaggle-higgs-competition-what-a-single-model-c