From 20dfcd7ceced716c8cb63aae117a7dc2cb84b45a Mon Sep 17 00:00:00 2001 From: pommedeterresautee Date: Tue, 14 Apr 2015 00:48:11 +0200 Subject: [PATCH] Add slides to readme + group documentation together --- README.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 6a05ce0c4..17edf4658 100644 --- a/README.md +++ b/README.md @@ -5,25 +5,27 @@ It implements machine learning algorithm under gradient boosting framework, incl Contributors: https://github.com/dmlc/xgboost/graphs/contributors -Turorial and Documentation: https://github.com/dmlc/xgboost/wiki - Issues Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion) Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) to ask questions and share your experience on xgboost. Examples Code: [Learning to use xgboost by examples](demo) -Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R](https://www.youtube.com/watch?v=Og7CGAfSr_Y) - Distributed Version: [Distributed XGBoost](multi-node) Notes on the Code: [Code Guide](src) +Turorial and Documentation: https://github.com/dmlc/xgboost/wiki + +Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R](https://www.youtube.com/watch?v=Og7CGAfSr_Y) + Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf) * This slide is made by Tianqi Chen to introduce gradient boosting in a statistical view. * It present boosted tree learning as formal functional space optimization of defined objective. * The model presented is used by xgboost for boosted trees +Presention of a real use case of XGBoost to prepare tax audit in France: [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit) + What's New ========== * [Distributed XGBoost now runs on YARN](multi-node/hadoop)!