本文详细介绍了XGBoost的参数功能，包括booster、eta、gamma、max_depth、min_child_weight等，帮助理解如何防止过拟合并调整模型复杂度。
XGBoost参数有很多，但实际上用上的有限。现根据训练XGBoost输出参数详细介绍参数功能。XGBoost官网参数介绍http://xgboost.readthedocs.io/en/latest/parameter.html#
1.General Parameters
booster [default=gbtree]
which booster to use, can be gbtree, gblinear or dart. gbtree and dart use 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]
number of parallel threads used to run xgboost
2.Parameters for Tree Booster
eta [default=0.3, alias: learning_rate]
step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features . and eta actually shrinks the feature weights to make the boosting process more conservative.
range: [0,1]
gamma [default=0, alias: min_split_loss]
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