将xgboost.Booster的实例转换为实现scikit-learn API的模型

import xgboost as xgb
from xgboost import XGBClassifier, XGBRegressor
from sklearn.datasets import load_iris, load_boston
from sklearn.model_selection import train_test_split

# 分类问题示例
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

# 回归问题示例
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=42)

# 分类问题示例
booster = xgb.train(params, dtrain)  # 使用自定义的参数和训练数据训练xgboost.Booster实例

# 回归问题示例
booster = xgb.train(params, dtrain)  # 使用自定义的参数和训练数据训练xgboost.Booster实例

# 分类问题示例
model = XGBClassifier()
model._Booster = booster  # 将xgboost.Booster实例赋值给XGBClassifier的_Booster属性

# 回归问题示例
model = XGBRegressor()
model._Booster = booster  # 将xgboost.Booster实例赋值给XGBRegressor的_Booster属性

y_pred = model.predict(X_test)  # 进行预测
score = model.score(X_test, y_test)  # 计算模型得分