Unsupervised Learning Flashcards

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

Answer: Silhouette score

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

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

Answer: The optimal number of clusters

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

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

Answer: t-SNE

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

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

Answer: Expectation-Maximization (EM)

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

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

Answer: It can find arbitrarily shaped clusters and identify outliers

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

In PCA, what does each principal component represent?

Answer: A direction of maximum variance in the data

Each principal component is an orthogonal direction in feature space along which the data has the most variance.

Which of the following is a hard clustering method?

Answer: K-Means

K-Means assigns each point to exactly one cluster (hard assignment), unlike soft/probabilistic methods that assign fractional membership.