title: High error in neuralnet
body: <p>I am trying to train a neural network using neuralnet in R, but am getting very high error terms (in the region of 1850).  The input variables are responses to a set of 6 likert scales (all on a 1-7) and the output is whether there was a topbox response to another variable (coded 0,1).  The input variables have been scaled to a 0,1 range (I've also tried normalizing to a mean of 0). I've tried a range of hidden nodes (from 1-10) and the network is converging to a threshold of 0.1 fairly reliably in 200000-400000 iterations, but with a consistent error term around 1800-1900. There are 30,000 cases in total, about 22000 in the training set. I appreciate that this type of problem doesn't need a neural network necessarily - this is proof of concept (going excellently...) on a familiar dataset before application to other questions. Any suggestions on how to reduce the error/improve the training net appreciated.</p>
  
  <p>As I said, I have tried both normalising and scaling, and now also using the pca preprocessing provided in caret. Must be something in the data, but at a bit of a loss...</p>
  
  <p>Code:</p>
  
  <pre><code>maxs &lt;-apply(final, 2, max)
  mins &lt;-apply(final, 2, min)
  
  scaled &lt;-as.data.frame(scale(final, center=mins, scale=maxs-mins))
  index&lt;-sample(1:nrow(scaled), round(0.75*nrow(scaled)))    
  
  train_ &lt;-scaled[index,]
  test_ &lt;-scaled[-index,]
  
  
  nn&lt;-neuralnet(Q11~A1+B1+C1+D1+E1+Q12, data=train_, hidden=5, rep=1,threshold=0.01, stepmax=6e+05, linear.output=F, lifesign='full')
  </code></pre>