MS-DS Master of Data science MS-DS Master of Data science Unsupervised Learning Techniques Questions and Answers 2

Question 1

Which unsupervised learning technique reduces dimensionality by finding orthogonal axes that maximize variance in the data?

Accepted Answer

Principal Component Analysis (PCA)

Answer

K-Means Clustering

Answer

DBSCAN

Answer

Apriori Algorithm

Question 2

In DBSCAN, what is a 'core point'?

Accepted Answer

A point with at least MinPts neighbors within radius epsilon

Answer

The centroid of each cluster

Answer

A point equidistant from two cluster centers

Answer

The first point selected during initialization

Question 3

What does the silhouette coefficient measure in clustering evaluation?

Accepted Answer

How similar a point is to its own cluster compared to the nearest neighboring cluster

Answer

The total within-cluster sum of squares

Answer

The number of optimal clusters using the elbow method

Answer

The ratio of between-cluster variance to total variance

Question 4

Which technique is most appropriate for discovering hierarchical groupings without specifying the number of clusters in advance?

Accepted Answer

Agglomerative hierarchical clustering

Answer

K-Means clustering

Answer

Gaussian Mixture Models

Answer

Self-Organizing Maps

Question 5

In a Gaussian Mixture Model (GMM), what algorithm is typically used to estimate the model parameters?

Accepted Answer

Expectation-Maximization (EM)

Answer

Gradient Descent

Answer

Forward-Backward Algorithm

Answer

Stochastic Gradient Descent

Question 6

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

Accepted Answer

Visualizing high-dimensional data in two or three dimensions

Answer

Clustering data into discrete groups

Answer

Performing feature selection

Answer

Training an autoencoder network