decision trees

Trees are the building block of the ensembles that rule tabular ML, so interviewers check you can explain how a split gets chosen and how a lone tree overfits. The Gini-vs-entropy part is a trap: candidates over-weight a choice that barely matters. Here is the answer.

Anyone can say 'ensemble of trees'. The real answer concerns decorrelating those trees and why that, not averaging alone, is what drops variance. Here is what makes the forest more than the sum of its trees.