MS-DS Master of Data science Unsupervised Machine Learning Models 3

Question 1

What does the Hopkins statistic measure in cluster analysis?

Accepted Answer

The spatial randomness of data — whether data is clusterable at all

Answer

The Hopkins statistic tests whether data has a significantly non-random spatial structure, indicating whether it is worth clustering.

Question 2

Which of the following is a key limitation of Principal Component Analysis (PCA) when applied to image data?

Accepted Answer

PCA assumes linear relationships and cannot capture nonlinear structure in image manifolds

Answer

PCA is a linear method and fails to capture nonlinear manifold structure present in image data, which is why kernel PCA or autoencoders are preferred.

Question 3

In the context of self-organizing maps (SOMs), what does the neighborhood function determine?

Accepted Answer

How much surrounding neurons are updated when a best-matching unit is found

Answer

The neighborhood function (typically Gaussian) controls how strongly neurons adjacent to the best-matching unit are pulled toward the input during each update.

Question 4

What is the 'cold start' problem in collaborative filtering?

Accepted Answer

Inability to make recommendations for new users or items with no interaction history

Answer

The cold start problem occurs when the system cannot generate recommendations for new users or items because no historical interaction data exists.

Question 5

Which property distinguishes agglomerative from divisive hierarchical clustering?

Accepted Answer

Agglomerative starts with each point as its own cluster and merges upward; divisive starts with one cluster and splits

Answer

Agglomerative clustering is bottom-up (merging), while divisive is top-down (splitting); both produce dendrograms but differ in direction.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Unsupervised Machine Learning Models 3