I have been running ANN (Neural network) on my data set, until last week that I figured out I will get more robust model using Cross validation. So that's why I have started using ANN with the aid of cross validation.
For example using a 10-fold cross validation, all the dataset will be divided into 10 sunsets and each time one of the subsets is being used as test set while the rest is being used as training set. so 10 models in being built based on the 10-fold cross validation, and at the end we have all them combined in one model (?). Regarding all these process I was expecting a smaller R-sq (r-sq obtained from predicted values vs. actual values) using "ANN with cross validation" Vs. "ANN without Cross validation". but the strange thing is the Cross validation ANN R-sq are bigger than the simple ANN (without cross validation ANN) ones. I was thinking that maybe sth is going wrong. I do not understand how, could you please help me with it?