move documentation to repo

doc/README.md

@@ -6,15 +6,24 @@ 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)
+
+How to get started
+====
+* Try to read the [binary classification example](../demo/binary_classification) for getting started example
+* Find the guide specific language guide above for the language you like to use
+* [Learning to use xgboost by example](../demo) contains lots of useful examples
 
 Highlights Links
 ====
 This section is about blogposts, presentation and videos discussing how to use xgboost to solve your interesting problem. If you think something belongs to here, send a pull request.
-* Blogpost by phunther: [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-can-do/) 
-* [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution) 
-* Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
-* 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)
+* [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-can-do/) 
+* Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
+* [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit)
+* [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)
 
 Contribution
 ====

doc/input_format.md

@@ -0,0 +1,51 @@
+Input Format
+====
+## Basic Input Format
+As we have mentioned, XGBoost takes LibSVM format. For training or predicting, XGBoost takes an instance file with the format as below:
+
+train.txt
+```
+1 101:1.2 102:0.03
+0 1:2.1 10001:300 10002:400 
+0 0:1.3 1:0.3
+1 0:0.01 1:0.3
+0 0:0.2 1:0.3
+```
+Each line represent a single instance, and in the first line '1' is the instance label,'101' and '102' are feature indices, '1.2' and '0.03' are feature values. In the binary classification case, '1' is used to indicate positive samples, and '0' is used to indicate negative samples. We also support probability values in [0,1] as label, to indicate the probability of the instance being positive.
+
+## Group Input Format
+As XGBoost supports accomplishing [ranking task](https://github.com/tqchen/xgboost/wiki/Ranking), we support the group input format. In ranking task, instances are categorized into different groups in real world scenarios, for example, in the learning to rank web pages scenario, the web page instances are grouped by their queries. Except the instance file mentioned in the group input format, XGBoost need an file indicating the group information. For example, if the instance file is the "train.txt" shown above,
+and the group file is as below:
+
+train.txt.group
+```
+2
+3
+```
+This means that, the data set contains 5 instances, and the first two instances are in a group and the other three are in another group. The numbers in the group file are actually indicating the number of instances in each group in the instance file in order.
+While configuration, you do not have to indicate the path of the group file. If the instance file name is "xxx", XGBoost will check whether there is a file named "xxx.group" in the same directory and decides whether to read the data as group input format.
+
+## Instance Weight File
+XGBoost supports providing each instance an weight to differentiate the importance of instances. For example, if we provide an instance weight file for the "train.txt" file in the example as below:
+
+train.txt.weight
+```
+1
+0.5
+0.5
+1
+0.5
+```
+It means that XGBoost will emphasize more on the first and fourth instance， that is to say positive instances while training.
+The configuration is similar to configuring the group information. If the instance file name is "xxx", XGBoost will check whether there is a file named "xxx.weight" in the same directory and if there is, will use the weights while training models. Weights will be included into an "xxx.buffer" file that is created by XGBoost automatically. If you want to update the weights, you need to delete the "xxx.buffer" file prior to launching XGBoost. 
+
+## Initial Margin file
+XGBoost supports providing each instance an initial margin prediction. For example, if we have a initial prediction using logistic regression for "train.txt" file, we can create the following file:
+
+train.txt.base_margin
+```
+-0.4
+1.0
+3.4
+```
+XGBoost will take these values as intial margin prediction and boost from that. An important note about base_margin is that it should be margin prediction before transformation, so if you are doing logistic loss, you will need to put in value before logistic transformation. If you are using XGBoost predictor, use pred_margin=1 to output margin values.

doc/parameter.md

@@ -11,7 +11,7 @@ In R-package, you can use .(dot) to replace under score in the parameters, for e
 
 ### General Parameters
 * booster [default=gbtree]
-  - which booster to use, can be gbtree or gblinear. The details about different boosters are described [here](https://github.com/dmlc/xgboost/wiki/Boosters). 
+  - which booster to use, can be gbtree or gblinear. gbtree uses tree based model while gblinear uses linear function.
 * silent [default=0]
   - 0 means printing running messages, 1 means silent mode.
 * nthread [default to maximum number of threads available if not set]