Xgboost overfitting

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# xgboost模型 params = { 'booster':'gbtree', 'objective':'multi:softmax', # 多分类问题 'num_class':10, # 类别数,与multi softmax并用 'gamma':0.1, # 用于控制是否后剪枝的参数,越大越保守,一般0.1 0.2的样子 'max_depth':12, # 构建树的深度,越大越容易过拟合 'lambda':2, # 控制模型复杂度的权重值的L2 正则化项参数,参数越大 ...

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Though both random forests and boosting trees are prone to overfitting, boosting models are more prone. Random forest build treees in parallel and thus are fast and also efficient. Parallelism can also be achieved in boosted trees. XGBoost 1, a gradient boosting library, is quite famous on kaggle 2 for its better results. It provides a parallel tree boosting (also known as GBDT, GBM).

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XGBoost is the leading model for working with standard tabular data (the type of data you store in Pandas DataFrames, as opposed to more exotic types of data like images and videos).

16 Recently, XGBoost has received a lot of attention. Hou et al. (2018) used single DT, RF, 17 GBM and XGBoost to predict roadway traffic flows and found similar accuracy across methods, with XGBoost requiring the lowest computing18 times. Wang (2018) used the 19 XGBoost, lightGBM and DTs methods to predict travel mode choices and found the