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Explain decision trees and tree ensembles

OneMain FinancialData Scientist · Technical Screen
EasyData ScientistTechnical ScreenMachine Learning

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This question evaluates understanding of decision trees, splitting criteria for classification versus regression, hyperparameter effects on the bias–variance trade-off, and the mechanics and rationale behind tree-based ensemble methods in the Machine Learning domain.

Prompt

  1. Explain how a decision tree works for classification or regression.
  2. How does the tree choose a split (objective functions for classification vs regression)?
  3. Name key hyperparameters and how they affect bias/variance.
  4. Pick a different ML algorithm that uses decision trees (e.g., Random Forest, Gradient Boosted Trees) and explain how it works and when you would choose it over a single tree.