I read that correlation is used to identify the relationship between variables and I have a few questions about the same, I was hoping someone could help me answer them.
- what is the use of knowing the relationship between correlated variables? Is it used in machine learning to identify and drop features that are highly related to reduce the feature space?
- Does removing highly correlated variables improve performance of the model and if yes, how?
- Is there anything else that it can be used for?