如何在Windows中安装XGBoost包

import xgboost as xgb
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# 加载数据集
boston = load_boston()
X, y = boston.data, boston.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建DMatrix
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

# 设置参数
params = {
    'objective': 'reg:squarederror',
    'max_depth': 4,
    'eta': 0.1
}

# 训练模型
num_rounds = 100
bst = xgb.train(params, dtrain, num_rounds)

# 预测
preds = bst.predict(dtest)

# 计算均方误差
mse = mean_squared_error(y_test, preds)
print(f"Mean Squared Error: {mse}")