This is the code to reproduce the results of Sigrist (2018) "Gradient and Newton Boosting for Classification and Regression". We compare gradient and Newton boosting, as well as hybrid gradient-Newton boosting with trees as base learners using various datasets and loss functions. See https://arxiv.org/abs/1808.03064 for more details.
Run the file 'Compare_boosting.py'. Running all experiments with all settings for real-world and simulated datasets takes some time. The parameter 'which_data' specifies which experiments are run. The file(s) 'results_summary_simulation=.csv' in the results folder contains the summary of the results.