Random Boosting builds on Friedman's Gradient Boosted Trees, but adds a new random component to the boosting procedure concerning the depth of a tree. More specifically, at each iteration, a random number between 1 and some upper limit is drawn that determines the maximally possible depth a tree can have at a certain step.
The algorithm is developed based on sklearn.ensemle.GradientBoostingRegressor and sklearn.ensemle.GradientBoostingClassifierand is used in exactly the same way (i.e. argument names match excactly and CV can be carried out with sklearn.model_selection.GridSearchCV). The only difference is that the RandomBoosting*-object uses max_depth to randomly draw tree depths for each iteration.
rb = RandomBoostingRegressor()
rb.fit(...)
rb.predict()simulation.py contains an example of how to use Random Boosting in regression.
Note that you can also use Random Boost by typing GradientBoostingRegressor(random_depth=True) or GradientBoostingClassifier(random_depth=True) (In fact, I implemented Random Boost as a sub- and wrapper class of the respective Gradient Boosting classes), which makes it usable as a simple add-on just like Stochastic Gradient Boosting.
Please feel free to test and give feedback on the algorithm. Also don't hesitate to contact me if you feel like it.