Explain Decision-Tree Training and Clustering Algorithms

Q: Explain Decision-Tree Training and Clustering Algorithms

This question evaluates understanding of supervised and unsupervised machine learning techniques, focusing on decision-tree training mechanics (split selection, stopping criteria, pruning and overfitting control) and the core principles behind clustering families such as partitioning, density-based, hierarchical and probabilistic methods.

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

Decision Trees and Clustering: Training Mechanics and Core Principles

Context

Technical/phone screen for an Applied Scientist/Data Scientist role, assessing foundational understanding of common machine-learning algorithms.

Tasks

(a) Explain how a decision-tree model is trained, including:

How split points are chosen.
Stopping criteria and pruning (pre-pruning vs. post-pruning).
How overfitting is avoided.

(b) Name at least three clustering algorithms and describe the core principle behind each (e.g., partitioning, density-based, hierarchical, probabilistic).

Explain Decision-Tree Training and Clustering Algorithms

Decision Trees and Clustering: Training Mechanics and Core Principles

Context

Tasks

Solution

Comments (0)