A demo for fitting hyper-parameters in Tree based ensemble regression methods such as gradient boosted regression and random forest classification.

To run this demo, you will need to download the news_popularity.p and naval_propulsion.p datasets into this directory. The datasets are available here. To run this demo, you will need to install scikit-learn.

Look at in_code_demo.py for a demo on how to use this in your code. Alternatively, run the following commands from this directory for gradient boosted regression on the naval propulsion dataset.

$ dragonfly-script.py --config config_naval_gbr.json --options ../options_files/options_example_realtime.txt
$ dragonfly-script.py --config config_naval_gbr_mf.json --options ../options_files/options_example_realtime.txt # For multi-fidelity version

 

References

• news_popularity.p Kelwin Fernandes, Pedro Vinagre, and Paulo Cortez. "A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News" Portuguese Conference on Artificial Intelligence, 2015.
• naval_propulsion.p Andrea Coraddu, Luca Oneto, Aessandro Ghio, Stefano Savio, Davide Anguita, and Massimo Figari. "Machine Learning Approaches for Improving Condition-based Maintenance of Naval Propulsion Plants", Journal of Engineering for the Maritime Environment, 2016.