python - 理解 python xgboost cv

Question

我想使用 xgboost cv 函数为我的训练数据集找到最佳参数。我对 api 感到困惑。如何找到最佳参数？这是否类似于 sklearn grid_search 交叉验证功能？我如何找到确定 max_depth 参数 ([2,4,6]) 的哪个选项是最佳的？

from sklearn.datasets import load_iris
import xgboost as xgb
iris = load_iris()
DTrain = xgb.DMatrix(iris.data, iris.target)
x_parameters = {"max_depth":[2,4,6]}
xgb.cv(x_parameters, DTrain)
...
Out[6]: 
   test-rmse-mean  test-rmse-std  train-rmse-mean  train-rmse-std
0        0.888435       0.059403         0.888052        0.022942
1        0.854170       0.053118         0.851958        0.017982
2        0.837200       0.046986         0.833532        0.015613
3        0.829001       0.041960         0.824270        0.014501
4        0.825132       0.038176         0.819654        0.013975
5        0.823357       0.035454         0.817363        0.013722
6        0.822580       0.033540         0.816229        0.013598
7        0.822265       0.032209         0.815667        0.013538
8        0.822158       0.031287         0.815390        0.013508
9        0.822140       0.030647         0.815252        0.013494

Answer 1

您可以通过 xgboost sklearn API 将 GridSearchCV 与 xgboost 一起使用

按如下方式定义分类器:

from xgboost.sklearn import XGBClassifier
from sklearn.grid_search import GridSearchCV 

xgb_model = XGBClassifier(other_params)

test_params = {
 'max_depth':[4,8,12]
}

model = GridSearchCV(estimator = xgb_model,param_grid = test_params)
model.fit(train,target)
print model.best_params_