Demonstrating how to use XGBoost accomplish binary classification tasks  on UCI mushroom dataset  http://archive.ics.uci.edu/ml/datasets/Mushroom

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/Binary-Classification