Decision trees and the machine learning models that are based on them, in particular random forests and gradient boosted trees, are fundamentally different types of models than generalized linear models, such as logistic regression. GLMs are rooted in the theories of classical statistics, which have a long history. The mathematics behind linear regression were originally developed at the beginning of the 19th century, by Legendre and Gauss. Because of this, the normal distribution is also called the Gaussian.
In contrast, while the idea of using a tree process to make decisions is relatively simple, the popularity of decision trees as mathematical models has come about more recently. The mathematical procedures that we currently use for formulating decision trees in the context of predictive modeling were published in the 1980s. The reason for this more recent development is that the methods used to grow decision trees rely on computational power – that is, the ability to crunch...