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我想在我的数据集中的 MLP Regressor 中实现交叉验证。我想知道我的代码是否正确？这是交叉验证的方式吗？这是我的示例数据：

Wavelength    Phase_velocity     Shear_wave_velocity
1.50              202.69          240.73
1.68              192.72          240.73
1.79              205.54          240.73
17.08             218               229
16.73             243               269
17.72             245               269
16.72             212               253
17.26             214               253
........

这是我的代码：

import pandas as pd
import numpy as np
from sklearn.model_selection import KFold,train_test_split,cross_val_score
from sklearn.neural_network import MLPRegressor

df = pd.read_csv("0.5-1.csv")
df.head()

X = df[['wavelength', 'phase velocity']]
y = df['shear wave velocity']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)

print (len(X_train),len(X_test),len(y_train),len(y_test))

from sklearn.model_selection import KFold
kfold = KFold(n_splits=207, shuffle=True)
mlp = MLPRegressor(hidden_layer_sizes=(500,500,500), max_iter=2000, activation='relu')
mlp.fit(X_train,y_train)
results = cross_val_score(mlp, X_train, y_train, cv=kfold) #Cross validation on training set

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)

print (mlp.predict(X_test)) #array_output
print (y_test)
print (mlp.score(X_test, y_test))
y_pred = mlp.predict(X_test)
from sklearn.metrics import mean_absolute_error
print (mean_absolute_error(y_test,y_pred))

from sklearn.metrics import mean_squared_error

from math import sqrt

rmse = sqrt(mean_squared_error(y_test,y_pred))

print(rmse)
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