小编B. *_*Sun的帖子

使用H2O Python模块AutoML之后，发现XGBoost位于页首横幅的顶部。然后，我想做的是从H2O XGBoost中提取超参数，并将其复制到XGBoost Sklearn API中。但是，这两种方法之间的性能有所不同：

from sklearn import datasets
from sklearn.model_selection import train_test_split, cross_val_predict
from sklearn.metrics import classification_report

import xgboost as xgb
import scikitplot as skplt
import h2o
from h2o.automl import H2OAutoML
import numpy as np
import pandas as pd

h2o.init()


iris = datasets.load_iris()
X = iris.data
y = iris.target

data = pd.DataFrame(np.concatenate([X, y[:,None]], axis=1)) 
data.columns = iris.feature_names + ['target']
data = data.sample(frac=1)
# data.shape

train_df = data[:120]
test_df = data[120:]

# Import a sample binary outcome train/test …