Repeated features in Neural Networks with tabular data When using algorithms like linear regression or least-squares methods, having repeated or highly correlated features can be harmful for the model. For tree based models, they are generally not too strongly affected by highly correlated features. There are no numeric stability issues as with least squares.
But what happens with Neural Networks? Most of the literature on NN is made for images, signal and there is not too much about tabular data.
Having repeated features in a tabular data Neural Network model, does it harm the accuracy? Or NN are able to select features?
Strictly theoretically it makes no difference on accuracy.
Here is why: We already know mathematically that NN can approximate any function. So lets say that we have Input X. X is highly correlated, than we can apply a decorrelation technique out there. Main Thing is, you get X` that has different numerical representation. Most likely more difficult for NN to learn to map to Outputs y. But still in Theory you can Change the architecure, Train for longer and you can still get the same Approximation, i.e. Accuracy.
Now, Theory and Praxis are same in Theory but different in Praxis, and I suspect that this Adjustments of Architecture etc will be much more costly in reality depending on the dataset.