I need to use neural network modeling for predicting variables and to avoid overfitting, I'm utilizing the K-fold cross-validation method. On the model screening platform, the neural network is fitted using boosting with three TanH functions. Is it possible to customize the neural network by using a different number and configuration than the platform’s default settings? I need to obtain the R-square value for each fold individually, and I also want to adjust the neural network settings, such as changing the number of functions and applying robust fitting.
