I'm training a NN with 8 features and 8000 training examples with a single output (0, 1) using the scipy.optimise CG algorithm and the results are somewhat inconsistent. The goal is to get the NN to be as 'precise' as possible (recall doesn't really matter too much) so I've set the threshold for y value quite high (0.75). Most of the time it gets a precision of around 80%, however sometimes it fails (using exactly the same parameters, lambda etc..) to generate any outputs which are above the 0.75 threshold, meaning the precision equals 0.
I've successfully trained NNs before without these unusual results (albeit the goal was a somewhat more conventional multi-class classifier with many more features).
I'm wondering if the training NNs with fewer features increases the chances of it getting stuck at a local optima; or getting stuck at local optima has a more significant impact on NNs with fewer features?
Any thoughts on what's going on!?