Data Science Data Science Unsupervised Learning Techniques Questions and Answers 2

Question 1

Which unsupervised learning technique reduces the dimensionality of data by finding principal components that maximize variance?

Accepted Answer

Principal Component Analysis (PCA)

Answer

K-Means Clustering

Answer

DBSCAN

Answer

Apriori Algorithm

Question 2

In DBSCAN clustering, what is a 'core point'?

Accepted Answer

A point with at least minPts neighbors within epsilon distance

Answer

The centroid of a cluster

Answer

A point that lies on the boundary of two clusters

Answer

The first point selected during initialization

Question 3

What does the silhouette coefficient measure in cluster analysis?

Accepted Answer

How similar a point is to its own cluster compared to neighboring clusters

Answer

The total number of clusters in the dataset

Answer

The variance explained by each cluster

Answer

The distance between cluster centroids

Question 4

Which unsupervised technique is most appropriate for detecting unusual transactions in credit card data without labeled fraud examples?

Accepted Answer

Anomaly detection using Isolation Forest

Answer

Linear regression

Answer

Naive Bayes classification

Answer

Decision tree classification

Question 5

What is the primary purpose of t-SNE in unsupervised learning?

Accepted Answer

Visualizing high-dimensional data in 2D or 3D space

Answer

Predicting continuous target variables

Answer

Performing feature selection

Answer

Building decision boundaries

Question 6

In hierarchical agglomerative clustering, what does 'complete linkage' use to measure distance between two clusters?

Accepted Answer

The maximum distance between any pair of points from the two clusters

Answer

The minimum distance between any pair of points from the two clusters

Answer

The average distance between all pairs of points

Answer

The distance between cluster centroids