9.6 Summary
BART is a flexible non-parametric model where a sum of trees is used to approximate an unknown function from the data. Priors are used to regularize inference, mainly by restricting trees’ learning capacity so that no individual tree is able to explain the data, but rather the sum of trees. PyMC-BART is a Python library that extends PyMC to work with BART models.
We built a few BART models in this chapter, and learned how to perform variable selection and use partial dependence plots and individual conditional plots to interpret the output of BART models.
