使用python api和scikit的XGBoost的不同结果-学习wapper

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

data = load_boston()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

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

params = {
    'objective': 'reg:squarederror',
    'eval_metric': 'rmse'
}

model = xgb.train(params, dtrain, num_boost_round=100)

y_pred = model.predict(dtest)
rmse = mean_squared_error(y_test, y_pred, squared=False)
print("RMSE:", rmse)