move documentation to repo

demo/regression/README

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-Demonstrating how to use XGBoost accomplish regression tasks on computer hardware dataset https://archive.ics.uci.edu/ml/datasets/Computer+Hardware
-
-Run: ./runexp.sh
-
-Format of input: LIBSVM format
-
-Format of ```featmap.txt: <featureid> <featurename> <q or i or int>\n ```:
-  - Feature id must be from 0 to number of features, in sorted order.
-  - i means this feature is binary indicator feature
-  - q means this feature is a quantitative value, such as age, time, can be missing
-  - int means this feature is integer value (when int is hinted, the decision boundary will be integer)
-
-Explainations: https://github.com/tqchen/xgboost/wiki/Regression

demo/regression/README.md

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+Regression
+====
+Using XGBoost for regression is very similar to using it for binary classification. We suggest that you can refer to the [binary classification demo](../binary_classification) first. In XGBoost if we use negative log likelihood as the loss function for regression, the training procedure is same as training binary classifier of XGBoost. 
+
+### Tutorial
+The dataset we used is the [computer hardware dataset from UCI repository](https://archive.ics.uci.edu/ml/datasets/Computer+Hardware). The demo for regression is almost the same as the [binary classification demo](../binary_classification), except a little difference in general parameter:
+```
+# General parameter
+# this is the only difference with classification, use reg:linear to do linear classification
+# when labels are in [0,1] we can also use reg:logistic
+objective = reg:linear
+...
+
+```
+
+The input format is same as binary classification, except that the label is now the target regression values. We use linear regression here, if we want use objective = reg:logistic logistic regression, the label needed to be pre-scaled into [0,1].
+