如何确定在scikitlearn MLPrepressor中培训和测试的是哪一个？

Question

我有两个数据集（训练和测试），它们都有完全相同的特性和标签列，只是内部不同（数字和值）。我的代码是：

import pandas as pd
import numpy as np 

from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor

datatraining = pd.read_csv("datatrain.csv")

datatesting = pd.read_csv("datatest.csv")

columns = ["Full","Id","Id & PPDB","Id & Words Sequence","Id & Synonyms","Id & Hypernyms","Id & Hyponyms"]

labeltrain = datatraining["Gold Standard"].values
featurestrain = datatraining[list(columns)].values


labeltest = datatesting["Gold Standard"].values
featurestest = datatesting[list(columns)].values

X_train = featurestrain
y_train = labeltrain

X_test = featurestest
y_test = labeltest

mlp = MLPRegressor(solver='lbfgs', hidden_layer_sizes=50, max_iter=1000, learning_rate='constant')

mlp.fit(X_train, y_train)

print('Accuracy training : {:.3f}'.format(mlp.score(X_train, y_train)))
print

mlp.fit(X_test, y_test)

print('Accuracy testing : {:.3f}'.format(mlp.score(X_test, y_test)))
print

我仍然怀疑我的代码是否能正确地找到训练和测试分数，因为我看不到区分函数来确定哪一个是训练的，哪个是测试的。我看到他们都在训练，或者都在测试。 谁能解释一下如何确定它？或者我的代码已经正确了吗？谢谢

Answer 1

一旦你的模型适合于培训，你就不应该再在测试中适合它了。相反，您应该使用测试集来评估模型的性能。因此，您需要删除该行

mlp.fit(X_test, y_test)

从你的代码。然后使用行

^{pr2}$

您将能够在看不见的数据上评估模型的性能。在