如何确定在 scikit-learn MLPRegressor 中哪个是训练和测试？

Question

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

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正确地找到了训练和测试分数，因为我看不到区分哪个是训练，哪个是测试。我看到两者都在训练，或者都在测试。 任何人都可以解释如何确定它？还是我的代码已经正确？谢谢

Answer 1

一旦将模型拟合到训练中，就不应再将其拟合到测试中。相反，您应该使用测试集评估模型的性能。因此，您需要删除该行

mlp.fit(X_test, y_test)

来自您的代码。然后使用行

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

您将能够评估模型在未知数据上的性能。