Data Science FREE Data Science Unsupervised Learning Techniques Questions and Answers 2

Question 1

Which unsupervised learning method groups data points by iteratively assigning them to the nearest centroid and updating centroids until convergence?

Accepted Answer

K-Means Clustering

Answer

K-Means works by alternating between assigning points to the closest centroid and recalculating centroids until assignments stabilize.

Question 2

What is the primary purpose of the elbow method in unsupervised learning?

Accepted Answer

Determining the optimal number of clusters

Answer

The elbow method plots within-cluster sum of squares against the number of clusters to identify where adding more clusters yields diminishing returns.

Question 3

In DBSCAN, what happens to data points that are neither core points nor reachable from any core point?

Accepted Answer

They are labeled as noise

Answer

DBSCAN classifies points that cannot be reached from any core point within the epsilon neighborhood as noise or outliers.

Question 4

Which technique reduces dimensionality by finding a lower-dimensional manifold that preserves local neighbor distances?

Accepted Answer

t-SNE

Answer

t-SNE preserves local pairwise distances when projecting high-dimensional data into two or three dimensions for visualization.

Question 5

What does the silhouette score measure in cluster analysis?

Accepted Answer

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

Answer

The silhouette score ranges from -1 to 1 and compares intra-cluster cohesion with inter-cluster separation for each data point.

Data Science Practice Test

Data Science Practice Test

Data Science FREE Data Science Unsupervised Learning Techniques Questions and Answers 2