Explain decision trees and tree ensembles

Q: Explain decision trees and tree ensembles

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.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Question

Prompt

Explain how a decision tree works for classification or regression.
How does the tree choose a split (objective functions for classification vs regression)?
Name key hyperparameters and how they affect bias/variance.
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.

Explain decision trees and tree ensembles

Prompt

Solution

Comments (0)

Explain decision trees and tree ensembles

Overview

Prompt

Solution

Comments (0)