我想使用xgboost cv函数来查找我的训练数据集的最佳参数.api我很困惑.我如何找到最佳参数？这与sklearn grid_search交叉验证功能类似吗？如何max_depth确定参数([2,4,6])的哪个选项最佳？
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 …