Output of Neural Net operator
I am having difficulty in understanding the output of rapid miner using the neural network operator.
Let me explain the task at hand and i really hope that someone may be able to assist me.
Firstly i am trying to predict the dwell time of a container into a port terminal.
I have big datasets with attributes like, DT ( Days), day of container release from the port (day of the week), month of container release from the port (month of the year) , type of container ( reefer, general cargo), size of container (20ft,40ft) and type of cargo transfered .
Appart from the DT (which is the value i want to predict) i have transformed all other data into dummies (0 not true,1 true)
With running linear regression i have a R^2= 0.165
With neural nets i have R^2= 0.249 with just 500 training cycles and all values in the operator set as default
and if i add training cycles and hidden layers (4 hidden layers and 10.000 training cycles) i got R^2= 0.363
However, i don't know whats the best number of hidden layers i need to use or how to interpret the output of the system.
I really would appreciate any help since thats the subject of my dissertation for my masters degree.
Thanks in advance.