Chapter 9
Bayesian Additive Regression Trees
Individually, we are one drop. Together, we are an ocean. – Ryunosuke Satoro
In the last chapter, we discussed the Gaussian process (GPs), a non-parametric model for regression. In this chapter, we will learn about another non-parametric model for regression known as Bayesian additive regression trees, or BART to friends. We can consider BART from many different perspectives. It can be an ensemble of decision trees, each with a distinct role and contribution to the overall understanding of the data. These trees, guided by Bayesian priors, work harmoniously to capture the nuances of the data, avoiding the pitfall of individual overfitting. Usually, BART is discussed as a standalone model, and software that implements it is usually limited to one or a few models. In this chapter, we will take a different approach and use PyMC-BART, a Python library that allows the use of BART models within PyMC.
In this chapter, we will cover the...
