代码的运行有一点小问题

import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklearn import preprocessing
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
# 读取含有热误差数据的CSV文件
train_file = open('data.csv', encoding='utf-8')
train_df = pd.read_csv(train_file)
 
# 读取测试集的含有热误差数据的CSV文件
test_file = open('data1.csv', encoding='utf-8')
test_df = pd.read_csv(test_file)
 
# 对训练集进行数据预处理
X_train = train_df.iloc[:, :-1]
Y_train = train_df.iloc[:, -1]
X_train_scaled = preprocessing.scale(X_train)
 
# 对测试集进行数据预处理
X_test = test_df.iloc[:, :-1]
Y_test = test_df.iloc[:, -1]
X_test_scaled = preprocessing.scale(X_test, with_mean=X_train_scaled.mean(axis=0), 
                                    with_std=X_train_scaled.mean(axis=0))
X = X_train + X_test
Y = Y_train + Y_test
# 创建决策树模型
dt = DecisionTreeRegressor()
 
# 定义网格搜索参数
param_grid = {
    'max_depth': [1,2,3,4,5,6,7,8,9],
    'min_samples_split': [2, 4, 6],
    'min_samples_leaf': [1, 2, 3]
}
# 进行网格搜索优化
grid = GridSearchCV(dt, param_grid, cv=5)
grid.fit(X, Y)
grid_search = GridSearchCV(DecisionTreeRegressor(), param_grid, cv=5)
grid_search.fit(X_train, Y_train)
best_model = grid_search.best_estimator_
 
# 输出最优参数和模型得分
print('Best Parameters:', grid.best_params_)

# 输出预测结果
Y_pred = best_model.predict(X_test)
mse = mean_squared_error(Y_test, Y_pred)
print(f"MSE: {mse:.4f}")
print(Y_pred)

下面的问题怎么解决

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

这个问题通常是因为使用了 NumPy 数组而不是标量值进行布尔运算导致的。在这个代码中，可能是 with_mean 和 with_std 这两个参数的值被传递为了 NumPy 数组而不是标量值，所以导致了这个错误。为了解决这个问题，可以尝试将这些参数的值转换为标量值。例如，将这些参数的值修改为以下代码中的相应部分：

X_test_scaled = preprocessing.scale(X_test, with_mean=X_train_scaled.mean(axis=0)[0], with_std=X_train_scaled.mean(axis=0)[0])

这里使用了索引 [0] 来获取 NumPy 数组中的第一个元素，将其转换为标量值。同样的方法也可以用于 with_std 参数。