Machine Learning Unsupervised Learning 2

Question 1

Which metric is commonly used to evaluate the quality of clusters when ground truth labels are unavailable?

Accepted Answer

Silhouette score

Answer

The silhouette score measures how similar a point is to its own cluster compared to other clusters, requiring no ground truth labels.

Question 2

What does the 'elbow method' help determine in K-Means clustering?

Accepted Answer

The optimal number of clusters

Answer

The elbow method plots inertia vs. number of clusters and identifies the point where adding more clusters yields diminishing returns.

Question 3

Which dimensionality reduction technique is specifically designed to preserve local neighborhood structure?

Accepted Answer

t-SNE

Answer

t-SNE (t-distributed Stochastic Neighbor Embedding) focuses on preserving local pairwise distances and is widely used for visualization.

Question 4

In Gaussian Mixture Models (GMM), what algorithm is used to estimate the parameters?

Accepted Answer

Expectation-Maximization (EM)

Answer

The EM algorithm alternates between the E-step (computing soft cluster assignments) and M-step (updating parameters) to fit GMMs.

Question 5

What is the primary advantage of DBSCAN over K-Means clustering?

Accepted Answer

It can find arbitrarily shaped clusters and identify outliers

Answer

DBSCAN identifies clusters of arbitrary shape based on density and automatically marks sparse points as noise/outliers.

Machine Learning Practice Test