Is it true that **a bias is said to be inductive iff it is *useful* in generalising the data**?

Or does inductive bias can also refer to the assumptions that may cause a decrease in performance?

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Suppose I have a dataset on which I want to use a deep neural network to do my task. I think, based on some knowledge, that a DNN with 5 or 11 layers may work well. But, after implementation, suppose 11 layer one worked well. Then can I call both of them inductive bias? or only the 11 layer assumption?