在分类列上训练xgboost时遇到问题

import xgboost as xgb
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import accuracy_score
from imblearn.over_sampling import SMOTE

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

# 模拟数据不平衡
y[y != 0] = -1
y[y == 0] = 1

# 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)

# 处理数据不平衡
smote = SMOTE(random_state=42)
X_train_res, y_train_res = smote.fit_resample(X_train, y_train)

# 定义XGBoost分类器
model = xgb.XGBClassifier(objective='binary:logistic', random_state=42)

# 定义参数网格
param_grid = {
    'max_depth': [3, 5, 7],
    'learning_rate': [0.1, 0.01],
    'n_estimators': [50, 100, 200]
}

# 网格搜索
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=3, scoring='accuracy', n_jobs=-1)
grid_search.fit(X_train_res, y_train_res)

# 输出最佳参数
print("Best parameters found: ", grid_search.best_params_)

# 使用最佳参数进行预测
best_model = grid_search.best_estimator_
y_pred = best_model.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: ", accuracy)