I heard people ask which one is better: Linear regression with regularization or Random Forest. My question is why can't you use regularization with Random Forest?
My understanding is that different regularization technique is adding a term to cost functions such as cross-entropy to reduce accuracy/overfitting to training data.
Typically, preventing overfitting technique for decision trees is associated with using Random Forest. I have never heard people associate regularization with decision trees or Random Forests.
Is it that reducing overfitting at the individual node level is too complex?
Edit: I acknowledge that RF is already a regularization method in a sense for decision trees through bootstrapping and aggregating. I think RF can be imaged as trying to estimate the answer from aggregating answers from crowd versus an expert(aka wisdom of the crowd). I am not asking what regularization for RF is, I am asking why can't we simply also apply regulation at a single node step of the decision tree, since it is just adding bias to the cross-entropy, which is the cost function for linear regression or the decision used to split a tree. Is it because there is no point doing that since we don't use cross-entropy to update values of the feature?