Data Science with Python Certification Data Science with Python Unsupervised Learning Algorithms 3

Question 1

What is the primary purpose of the elbow method in K-Means clustering?

Accepted Answer

Selecting the optimal number of clusters by identifying the point of diminishing returns in inertia

Answer

The elbow method plots inertia vs. k and looks for the 'elbow' where adding more clusters yields minimal improvement.

Question 2

What is the key assumption of the Gaussian Mixture Model (GMM)?

Accepted Answer

Data is generated from a mixture of Gaussian distributions

Answer

GMM assumes each data point is generated from one of several Gaussian distributions with unknown parameters.

Question 3

Which step in the Expectation-Maximization (EM) algorithm computes the probability of each point belonging to each cluster?

Accepted Answer

E-step (Expectation)

Answer

The E-step computes posterior probabilities (responsibilities) of cluster membership for each data point.

Question 4

In scikit-learn, which parameter of `KMeans` controls the number of times the algorithm runs with different centroid seeds?

Accepted Answer

n_init

Answer

`n_init` specifies how many times K-Means is run with different centroid seeds; the best result is kept.

Question 5

What is 'whitening' in the context of PCA preprocessing?

Accepted Answer

Scaling principal components to have unit variance

Answer

Whitening divides each principal component by its standard deviation so all components have unit variance.

Data Science with Python Certification Practice Test