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

PCA identifies principal components as orthogonal directions of maximum variance, enabling dimensionality reduction while preserving the most information.

Question 2

In DBSCAN, what is a 'core point'?

Accepted Answer

A point with at least MinPts neighbors within radius epsilon

Answer

DBSCAN defines a core point as one that has at least MinPts data points within its epsilon-radius neighborhood, forming the dense regions of clusters.

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 silhouette coefficient ranges from -1 to 1 and compares intra-cluster cohesion with nearest-cluster separation for each data point.

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

Agglomerative hierarchical clustering builds a dendrogram by iteratively merging the closest clusters, allowing the analyst to choose the number of clusters after the fact.

Question 5

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

Accepted Answer

Expectation-Maximization (EM)

Answer

The EM algorithm iterates between computing soft cluster assignments (E-step) and updating the Gaussian parameters to maximize likelihood (M-step).

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

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