How does a decision tree choose splits, and what is the difference between Gini impurity and entropy?
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.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
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.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.