What actually happened:
Actually I get confused of the process :
First : we instantiate the model & working on it
second : we used cross validation & instantiate another model & working on it
Third : after check our model accuracy with the accuracy of cross validation we instantiate another model like the first one & used it on final prediction
Now I have the following question :
1- should I follow these process on any model , I mean instantiate the model first then use cross validation ?
2- cross validation model is only useful on comparing accuracy & to make final prediction should I use the original model without cross validation?
3- What if the accuracy of cross validation was far than the original model what should I do ?