I have a neural net that's generating an average 15% error across the three outputs it gives. My problem is two of the three really have about a 2% error while the third has around 40%.
I was wondering if anyone has used results from a correlation matrix on the raw data to set starting parameters for what features are valued more relative to the labels. If this is possible, or logical, how would you initialize this for two input features if your model's first layer has say eight nodes - or at least more than two? I'm using Keras btw, but even a theoretical explanation would be helpful.
If it doesn't make sense to follow this path, does anyone have recommendations at correctly the imbalance?