Here are examples on how to use the gpboost Python package. You should install the GPBoost Python package first. You also need scikit-learn and matplotlib (for plots) to run the examples, but they are not required for the package itself. You can install these packages with pip:

pip install scikit-learn matplotlib -U

It is recommended that you run the examples in interactive mode using, e.g., Spyder or PyCharm. Alternatively, you can run the examples from the command line in this folder, for example:

python boosting_example.py

Examples include:

• GPBoost and LaGaboost algorithms for Gaussian data ("regression") and non-Gaussian data ("classification", etc.) combining tree-boosting with Gaussian process and random effects models
• Parameter tuning using deterministic or random grid search
• Generalized linear Gaussian process and mixed effects model examples
• GPBoost algorithm applied to panel data