In R, there is a package called mice which multiply imputes a dataset.
For my situation, I imputed using the predictive mean matching (pmm) method using a time series that I acquired that details multiple different climate variables like average temperature, relative humidity, precipitation etc.
At some point I would like to combine the results of these multiply imputed datasets, but in the syntax of the mice packages in R I apparently need to fit a statistical model on each imputed dataset.
But why do I need this? I mean sure, I need to run a model on the completed dataset (artificial neural networks), but that comes after.
Examples on the internet use the lm() package, but that's not exactly what I need.
What should I do and why should I do it?
Example on the internet: https://datascienceplus.com/imputing-missing-data-with-r-mice-package/
Thanks to anyone who replies.