What does a cross (horizontal line) in the regression plot of a neural network mean with multivariant input and output?
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I have trained a neural network and got the below regression plot.
First of all I have nomalized every sample, by substracting its mean value over the samples and dividing this with its standard deviation. So that all input and output is normalized and in the same range. Is that allowed, or do I introduce any errors into my data.?
I have tried several network structures and always get such a cross in the regression plot, but as I guess, some of the output data seems to be insensitive to the input data. Is that right? If so, how can I check that.
Thanks for your help! Best regards,
Cris LaPierre on 2 Dec 2020
Edited: Cris LaPierre on 3 Dec 2020
Normalizing is a standard preprocessing step. It is helpful when you have several inputs to your model that are of different scale. It helps prevent any one feature from dominating the model due to its scale. When you have a single input, this is unnecessary. Also, this is for preprocessing. I don't think it makes sense to do this after the fact, and could be affecting your visualization.
A cross would suggest there are two different types of data in your data set-one with a relationship and one without. The horizontal part indicates data points that have no relationship between the Target and the Output.