[kikike]

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Got it:) Many thanks!

[kikike]

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Appreciate the detailed information. Yes , that is the theory of k fold cross-validation.

I think I confused you the question.

My question is :

1. We have a model already(we need dataset to find our final model, let’s say this process we use dataset A ).

2. Then we need to assess predicting performance of our model. We can use k fold cross-validation to do it. During this process, we still need dataset, let’s say this process we use dataset B.

The question is what is dataset A, and what is dataset B ?

For example, A and B come from the same original dataset? Let ‘s say 70% of original dataset used for A, and 30% of original dataset used for B?

That is what I want to ask, help it is much clear now.

[kikike]

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Many thanks, so for k fold cross cross-validation, how do we split data for model building and for assess predicting performance?

[kikike]

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Thanks for quick reply:)

So we use train data to find the final model, and use the validation for assess the predicting performance?

I saw the following R code, is that used for assess the predicting performance?

set.seed(1) # set the random number generator seed

cv.error.10 = cv.glm(validate, finalmodel, K=10) #here, does validate means the validate dataset?

paste(“The cv error for final model is”, signif(cv.error.10$delta[1], digits=3))