import xgboost as xgb
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import numpy as np
 
# 加载数据集，切分训练集和测试集，占比7:3 
boston = load_boston()  # 获取波士顿房价数据集  
X, y = boston.data, boston.target    # 分割特征和目标变量  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)   # 切分训练集和测试集，占比7:3 

  # XGBoost训练过程 

    xgbregressor = xgb.XGBRegressor(maxdepth=5, learningrate=0.1)   # 构造XGboost回归模型对象  

    xgbregressor.fit(X=X_train,y=y_train)   # 训练模型   

    preds = xgbregressor.predict(X=X)      # 预测