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I have learned the basic method of cross-validation, but I have a question. Before cross-validation, how to build a logistic regression model fist? I mean that should I use the complete data set to build logistic regression model(not split data to training set and validation set), and then use the complete data set for K Fold cross-validation?

For example: My data set is named mydata, and then use the complete data set to filter variables and build logistic regression model, finally, use the model and complete data set to perform K-fold cross-validation, is this process correct?

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  • $\begingroup$ It sounds like you're confused re: what the main point of using cross validation is. I'd be happy to help but I think it would be a worthwhile exercise for you to think about why you need CV in the first place. $\endgroup$
    – Adrià Luz
    Sep 25, 2021 at 14:05
  • $\begingroup$ I'm a new in cross-validation, and I am also trying to read more articles and blogs about cross-validation, but I'm still don’t know how to get the final model with CV. Is there any R code for the whole process of obtaining the final model through cross-validation? Maybe I can further understand the principles of cross-validation in modeling regression models. $\endgroup$
    – dbcoffee
    Sep 25, 2021 at 15:38
  • $\begingroup$ Are you using cross validation to tune your model's hyperparameters too? Or only to understand how well it will generalise to unseen data? $\endgroup$
    – Adrià Luz
    Sep 25, 2021 at 15:57

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Cross validation is intended to estimate the generalization error of the model building process you’re using.

You don’t build a model prior to doing cross validation. In each of the folds, you build a model doing everything you intend to do on the training data (including variable selection). Each model built on the K folds predicts on the held out part of the data to obtain an error or loss metric. The average of those error/loss metrics is an estimate of the error/loss metric for that model building process

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  • $\begingroup$ Thank you for your explanation, but after reading more articles, I still don’t quite understand how to get the final model through cross-validation. $\endgroup$
    – dbcoffee
    Sep 25, 2021 at 15:43
  • $\begingroup$ @dbcoffee you don’t get the final model from cross validation. You get the final model by fitting your model to the training data. Cross validation is a means of estimating error on new data, not a means of fitting a model. $\endgroup$
    – Demetri Pananos
    Sep 25, 2021 at 15:55
  • $\begingroup$ I think I probably understand the purpose of cross-validation. The purpose of cross-validation is to evaluate the generalization ability of the model and not for the purpose to build model. Back to the question above, If I only have one data set mydata, can I use mydata to build a logistic regression model first, and then use mydata to perform K-fold cross-validation to evaluate the generalization ability of the model just created? If not, how can I build and verify the model with only one data set (small sample)? $\endgroup$
    – dbcoffee
    Sep 26, 2021 at 14:24

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