Can someone explain to me like I'm five on why multicollinearity does not affect neural networks?
I've done some research and neural networks are basically linear functions being stacked with activation functions in between, now if the original input variables are highly correlated, doesn't that mean multicollinearity happens?
Topic neural collinearity regression deep-learning neural-network
Category Data Science
Multicollinearity in linear or logistic regression does not impact the model performance it only impacts how model coefficient are interpreted. Multicollinearity leads to a problem where small change in one variable can lead to drastic changes in coefficients.
A neural network is black box model in nature so if its performs to a given expectation (good accuracy) we never know the impact of multicollinearity. Thats why i think most blogs say that neural network are not affected by multicollinearity
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