For more references on decision trees, random forests, gradient boosted forests, and the mathematics behind differentiability, smoothness, and continuity, we encourage the reader to read the following references.
- Tutorial on decision trees. From a machine learning crash course by Berkeley. https://ml.berkeley.edu/blog/2017/12/26/tutorial-5/
- Random forest python tutorial. By Chris Albon. https://chrisalbon.com/machine_learning/trees_and_forests/random_forest_classifier_example/
- A nice PDF presentation on convex functions, how they are used in machine learning, and the differences between smoothness, differentiability, and continuity. By Francis Bach. Also has ~6 pages of useful references at the end, which the reader may find helpful. ...
