在python中为xgboost超参数运行gridsearchcv时出错

import pandas as pd
from sklearn.model_selection import train_test_split

# 假设df是你的DataFrame
df = df.dropna()  # 删除缺失值
X = df.drop('target', axis=1)
y = df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV

# 定义参数网格
param_grid = {
    'max_depth': [3, 5, 7],
    'learning_rate': [0.1, 0.01],
    'n_estimators': [50, 100]
}

# 初始化XGBoost分类器
xgb = XGBClassifier()

# 初始化GridSearchCV
grid_search = GridSearchCV(estimator=xgb, param_grid=param_grid, cv=3, n_jobs=-1)

# 拟合模型
grid_search.fit(X_train, y_train)

pip install --upgrade scikit-learn xgboost

# 减少数据集大小
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)