Very good blog...
I'm doing some stuff with Clementine... and I have an issue...
My target for NN train dataset is a continuos value between 0 and 100... the problem is that is a normal/gaussian distribution and makes the NN predict bad...
How can I resolve the unbalancing data? split into classe with same frequency!?
I am not aware that neural networks have a problem with predicting values with normal distributions. In fact, if you randomize the weights in a neural network whose output layer has a linear transfer function, then the output is likely to follow a normal distribution -- just from the Central Limit Theorem of statistics.
So, you have a neural network that is not producing good results. There can be several causes.
The first thing to look for is too many inputs. Clementine has options to prune the input variables on a neural network. Be sure that you do not have too many inputs. I would recommend a variable reduction technique such as principal components, and advise you to avoid categorical variables that have many levels.
A similar problem can occur if your hidden layer is too large.
Whatever the network, it is worthwhile looking at the number of weights in the network (or a related measure called the degrees of freedom). Remember, you want to have lots of training data for each weight.
Another problem may be that the target is continuous, but bounded between 0 and 100. This could result in a neural network where the output layer uses a linear transfer function. Although not generally a bad idea, it may not work in this case because the range of a linear function is from minus infinity to positive infinity, which far exceeds the range of the data.
One simple solution would be to divide the output by 100 and treat it as a probability. The neural network should then be set up with a logistic function in the target layer.
Your idea of binning the results might also work, assuming that bins work for solving the business problem. Equal sized bins are reasonable, since they are readily understandable as quantiles.