I am using neuralnet package to run artificial neural network on my data set for prediction. The response variable have 4 levels and I have 73 independent variables that are all dummy (indicator) variables and they are relatively sparse (I mean their values are mostly 0 rather than 1). My sample size is around 50000. When I run neuralnet with 1 hidden layer and 1 node, it takes like 10 minutes. I use a validation data set for validation and the prediction accuracy of this model is very low. Also I use more than 1 layer and more than 1 node and it takes hours to run and finally the prediction accuracy is still very bad. I used other methods for prediction such as multinomial logistic regression, K-nearest neighbour, SVM, random forests, etc. and they all have way better prediction accuracy. I use the default algorithm, I increase the step max to 1e+09 and increase the threshold to 0.1 and still takes hours and hours.
Can you tell me what is the appropriate number for hidden layers and affiliated number of nodes to get good prediction accuracy and also how can I make it converge faster. Thank you so much. Fred