I'm using scikit-learn to build a decision tree (or a random forrest) for a regression problem. I have continuous variables as my regressors. I wonder to know how the splits in a regression decision tree in scikit-learn are being calculated? That is, how it searches through the space of all possible split points to determine which one is the best split point for a continuous variable? The implementation is based on CART.
I've seen some using the