diff --git a/README.md b/README.md index 4b6dafb58..bce629477 100644 --- a/README.md +++ b/README.md @@ -5,22 +5,14 @@ It implements machine learning algorithm under gradient boosting framework, incl Contributors: https://github.com/dmlc/xgboost/graphs/contributors +Documentations: [Documentation of xgboost](doc/README.md) + 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) - Distributed Version: [Distributed XGBoost](multi-node) -Notes on the Code: [Code Guide](src) - -Documentation: [Documentation of xgboost](doc/README.md) - -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 What's New ========== diff --git a/demo/README.md b/demo/README.md index 3b3261035..49e9e52b8 100644 --- a/demo/README.md +++ b/demo/README.md @@ -49,3 +49,4 @@ Benchmarks ==== * [Starter script for Kaggle Higgs Boson](kaggle-higgs) * [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution) + diff --git a/doc/README.md b/doc/README.md index 53b15973f..3021001fa 100644 --- a/doc/README.md +++ b/doc/README.md @@ -1,11 +1,13 @@ List of Documentations ==== -* [Parameters](parameter.md) * [Using XGBoost in Python](python.md) * [Using XGBoost in R](../R-package/vignettes/xgboostPresentation.Rmd) * [Learning to use xgboost by example](../demo) * [External Memory Version](external_memory.md) * [Text input format](input_format.md) +* [Notes on the Code](../src) +* [Parameters](parameter.md) +* Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf) How to get started ====