Data Science with Python Certification Data Science with Python Unsupervised Learning Algorithms 4

Question 1

Which of the following is NOT a valid initialization strategy for K-Means in scikit-learn?

Accepted Answer

'ward'

Answer

'ward' is a linkage criterion for hierarchical clustering, not a valid `init` parameter for `KMeans`.

Question 2

What does the Calinski-Harabasz index measure?

Accepted Answer

The ratio of between-cluster dispersion to within-cluster dispersion

Answer

A higher Calinski-Harabasz index indicates denser, well-separated clusters.

Question 3

In Isolation Forest, how are anomalies detected?

Accepted Answer

Anomalies are isolated in fewer splits than normal points

Answer

Isolation Forest isolates anomalies using random partitioning trees; anomalies require fewer splits because they differ from most points.

Question 4

What is the main advantage of using sparse PCA over standard PCA?

Accepted Answer

Sparse PCA produces components with few non-zero loadings, improving interpretability

Answer

Sparse PCA introduces sparsity constraints so each component depends on only a few original features, aiding interpretation.

Question 5

Which algorithm is best suited for clustering data with non-convex shapes?

Accepted Answer

DBSCAN

Answer

DBSCAN identifies clusters based on density, making it capable of finding arbitrarily shaped clusters.

Data Science with Python Certification Practice Test