Recently I am working on some predictive analytic which based on neural network.
When I tried some tests on MLP with one hidden layer or multiple hidden layers, the results showed that:
one hidden layer performance on prediction is always better than multiple hidden layers
the tests were executed both on Knime and R models, they gave me the same trends
Based on my knowledge, more hidden layers with more neurons should perform better?
Is there a principle on this about for which kind of dataset turns out this kind of result?
Or I may need some book / article / paper to read?
Do you have any abstract idea (not mathematical algorithms) for me, thanks!
I am working on a fraud detection dataset, which includes 1000 observations. and all the dimensions are numericed and normalized...
But I am actually asking for a general idea, for the general idea to choose algorithm for different dataset