So I'm applying Random forest regression from sklearn library to a dataset having only one feature and I'm getting a very good score. The output labels are continuous.
The problem is I don't quite understand how are the decision trees being built here. While building a decision tree we split at each node based on a feature. since we have only feature here, will the decision trees have only one node? How will the decision trees be different?