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？这是交叉验证的方法吗？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)