I have some doubts related to my neural network implementation. I have 750 features and 98 outputs. I have number of samples = 5000. Now I used cross validation to select the number of neurons in the hidden layer(1 hidden layer). I used 10 fold cross validation. I got that number of neurons = 30 performed the best which is kind of strange. I couldn't explain what's going on here. Any ideas/ suggestions?
How could just 30 neurons do better with such high dimensional data
I used tansig activation for the hidden layer and logsig for the output. The outputs used for training were scaled to the range of [0 1]. The inputs where standardized to zero mean and unit variance.