To understand decision tree-based models, let's try to imagine that Google wants to recruit people for a software development job. Based on the employees that they already have and the ones they have rejected previously, we can determine whether an applicant was from an Ivy League college or not and what the Grade Point Average (GPA) of the applicant was.
The decision tree will split the applicants into Ivy League and non-Ivy League groups. The Ivy League group will then be split into high GPA and low GPA so that people with a high GPA are likely to be tagged highly and the ones with a low GPA are likely to get recruited.
Applicants who have a high GPA and belong to non-Ivy League colleges have a slightly better chance of getting ...