Explain decision trees and tree ensembles
Company: OneMain Financial
Role: Data Scientist
Category: Machine Learning
Difficulty: easy
Interview Round: Technical Screen
## 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.
Quick Answer: 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.