Data Science FREE Data Science Unsupervised Learning Techniques Questions and Answers 3

Question 1

How do Gaussian Mixture Models differ from K-Means in their approach to clustering?

Accepted Answer

They use probabilistic soft assignments based on multiple Gaussian distributions

Answer

GMMs model each cluster as a Gaussian distribution and assign data points probabilistic memberships across all clusters rather than hard assignments.

Question 2

What is the main advantage of hierarchical agglomerative clustering over partitional methods?

Accepted Answer

It produces a dendrogram showing cluster relationships at every level of granularity

Answer

Agglomerative clustering builds a tree of merges (dendrogram) that lets analysts choose the number of clusters after seeing the full hierarchy.

Question 3

In Principal Component Analysis, what does the first principal component represent?

Accepted Answer

The direction of maximum variance in the data

Answer

The first principal component is the linear combination of original features that captures the greatest amount of variance in the dataset.

Question 4

What is a key limitation of using autoencoders for dimensionality reduction compared to PCA?

Accepted Answer

Autoencoders can overfit and require careful regularization and tuning

Answer

Autoencoders are neural networks that can memorize training data if not properly regularized, making them prone to overfitting on small datasets.

Question 5

Which evaluation metric for clustering does NOT require ground truth labels?

Accepted Answer

Davies-Bouldin Index

Answer

The Davies-Bouldin Index evaluates clustering quality using only within-cluster scatter and between-cluster distances, requiring no external labels.

Data Science Practice Test