I have some data containing several features, mainly continuous variables.
Implementing the randomForestRegressor algorithm from the sci-kit package in Python is relatively simple and results look OK. However, I need to know how the node splitting process is carried out by the randomForestRegressor algorithm.
I have been reading the documentation (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) and (https://scikit-learn.org/stable/modules/ensemble.html#forest) but have not been able to find a direct answer.
My assumption is, that if the dataset is given as is or minmax normalized (which is still continuous form for all variables, only from 0 to 1 values) at each node, the splitting process will be done in a binary way over a randomly selected subset of features, at a value which minimizes target variable variance in the resulting split nodes? but there are other possibilities...
Does anybody know which is the exact process carried out by this package?