Data Science with Python Certification Data Science with Python Unsupervised Learning Algorithms 5

Question 1

What problem does K-Means++ initialization solve compared to random initialization?

Accepted Answer

It reduces the chance of converging to a poor local minimum by spreading initial centroids

Answer

K-Means++ selects initial centroids probabilistically to be far apart, leading to better and faster convergence.

Question 2

Which dimensionality reduction technique preserves global structure better than t-SNE?

Accepted Answer

UMAP

Answer

UMAP better preserves global structure compared to t-SNE while still capturing local neighborhoods effectively.

Question 3

What does a Davies-Bouldin index close to 0 indicate?

Accepted Answer

Better clustering with well-separated, compact clusters

Answer

Lower Davies-Bouldin values indicate clusters that are compact internally and well-separated from each other.

Question 4

In Online/Mini-Batch K-Means, what is the key trade-off compared to standard K-Means?

Accepted Answer

Faster convergence and lower memory usage at the cost of slightly lower cluster quality

Answer

Mini-Batch K-Means uses random subsets of data per iteration, trading a small quality drop for significant speed and memory gains.

Question 5

Which of the following correctly describes 'soft clustering' in GMM versus 'hard clustering' in K-Means?

Accepted Answer

GMM assigns probabilities of belonging to each cluster; K-Means assigns each point to exactly one cluster

Answer

GMM produces probabilistic (soft) assignments where each point has a probability for every cluster, unlike K-Means' hard assignments.

Data Science with Python Certification Practice Test