Question
Hello, I'm trying to develop a neural network for classification of 2 non mutually exclusive outputs, based on 1 input. Using the standard parameters for the patternnet, I had in the 2 outputs numbers between 0 and 1, summing up to 1. It looked like it was working as they were mutually exclusive, even thought I had in my training matrix cases with both outputs in state 1. I changed the output transfer function to logsig and removed the default { -1, 1} mapminmax output transformation (after reading this in another topic), but with this I had a very poor training performance...
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Expert Answer
John Williams
PhD Expert
Answered Nov 20, 2025
The training target should have nonnegative entries that sum to 1 and can be interpreted as prior probabilities.
0 and 1 are only used if the classes are mutually exclusive.
The output target should also have nonnegative entries that sum to 1 (e.g., via SOFTMAX) and can be interpreted as posterior probabilities.
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