有关监督学习中培训和测试的问题
我有点困惑，希望有人可以帮助我。
我目前正在尝试监督的学习。而且我认为我对lstms的输入和输出有一个基本的误解。
当我有10个观测值的序列时，
我将其分为trains = 1,2,3,4,5,6,7,8
，test = 9,10代码>。
然后我将其转变为一个有监督的问题：
Xtrain= [(1,2)(2,3)(3,4)(4,5)(5,6)]
Ytrain= [(3,4)(4,5)(5,6)(6,7)(7,8)]
因此，
Xtest= [(7,8)]
该模型是为了预测前两个观察结果。
prediction <- predict(Xtest)
火车/测试拆分是非法的吗？我是否正确地说，我可以根据XTEST对包含的实际测试集评估预测输出
，或者我应该在xtrain停止培训= [（4,5）]和ytrain = [（6,7）]在训练和测试之间获得一些空间，因为我的示例中使用了Y培训中的最后一次观察结果为了预测 ？
I am a bit confused and hope someone can help me.
I am currently experimenting with supervised learning. And I think I have a basic misunderstanding about the input and output of LSTMs.
When I have a sequence of 10 observations,
And I split it into trains = 1,2,3,4,5,6,7,8
Also, test = 9,10.
And I transform it into a supervised problem like:
Xtrain= [(1,2)(2,3)(3,4)(4,5)(5,6)]
Ytrain= [(3,4)(4,5)(5,6)(6,7)(7,8)]
And
Xtest= [(7,8)]
So the model is made to predict the next two observations from the previous two.
prediction <- predict(Xtest)
Is this illegal for a train/test split ? Am I correct that I can than evaluate the prediction output from xtest against the actual test set containing [(9,10)]
Or should I stop training at xtrain =[(4,5)] and ytrain = [(6,7)] to get some space between training and testing, since the last observations from y training in my example are used for the prediction
?
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