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.

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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

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Explain Decision-Tree Training and Clustering Algorithms

Overview

Decision Trees and Clustering: Training Mechanics and Core Principles

Context

Tasks

Solution

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