Question #7
Reading: Reading 3 Machine Learning
PDF File: Reading 3 Machine Learning.pdf
Page: 3
Status: Unattempted
Correct Answer: A
Question
The unsupervised machine learning algorithm that reduces highly correlated features into fewer uncorrelated composite variables by transforming the feature covariance matrix best describes:
Answer Choices:
A. k-means clustering
B. hierarchical clustering
C. principal components analysis
Explanation
Principal components analysis (PCA) is an unsupervised machine learning algorithm that
reduces highly correlated features into fewer uncorrelated composite variables by
transforming the feature covariance matrix. K-means partitions observations into a fixed
number (k) of non-overlapping clusters. Hierarchical clustering is an unsupervised iterative
algorithm used to build a hierarchy of clusters.