Question #16 - Reading 3 Machine Learning

Reading: Reading 3 Machine Learning

PDF File: Reading 3 Machine Learning.pdf

Page: 6

Status: Unattempted

Shared Context

- Nowak first tries to explain classification and regression tree (CART) to Kowalski. CART is least likely to be applied to predict a: A) discrete target variable, producing a cardinal tree. B) continuous target variable, producing a regression tree. C) categorical target variable, producing a classification tree.

Question

Which of the following statements Nowak makes about hierarchical clustering is most accurate?

Answer Choices:

A. Bottom-up hierarchical clustering begins with each observation being its own cluster

B. In divisive hierarchical clustering, the algorithm seeks out the two closest clusters

C. Hierarchical clustering is a supervised iterative algorithm that is used to build a hierarchy of clusters

Explanation

Agglomerative (bottom-up) hierarchical clustering begins with each observation being its own cluster. Then, the algorithm finds the two closest clusters, and combines them into a new, larger cluster. Hierarchical clustering is an unsupervised iterative algorithm. Divisive (top-down) hierarchical clustering progressively partitions clusters into smaller clusters until each cluster contains only one observation.

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