MS-DS Master of Data science Unsupervised Machine Learning Models 5

Question 1

When applying k-means to categorical data, which variant is the appropriate alternative?

Accepted Answer

k-modes, which uses mode instead of mean and Hamming-based dissimilarity

Answer

k-modes replaces the mean with the mode for categorical variables and uses a dissimilarity measure like simple matching, making it suitable for categorical data.

Question 2

In matrix factorization for collaborative filtering, what do the latent factors typically represent?

Accepted Answer

Latent features capturing abstract user preferences and item attributes that explain observed ratings

Answer

Latent factors are learned abstract representations (e.g., genre preference, quality sensitivity) that explain the pattern of observed ratings without explicit feature engineering.

Question 3

What is the 'elbow method' limitation when used to select k for k-means clustering?

Accepted Answer

The elbow is often ambiguous or absent, making objective k selection difficult in practice

Answer

Real-world inertia curves often decrease smoothly without a clear inflection point, making the elbow subjective and unreliable for automated k selection.

Question 4

Isolation Forest detects anomalies by:

Accepted Answer

Measuring how few random splits are needed to isolate a point in a random tree ensemble

Answer

Anomalies are isolated quickly (few splits) because they are sparse and distinct, while normal points require many splits to isolate in randomly partitioned trees.

Question 5

In the context of autoencoders used for anomaly detection, a high reconstruction error for a test sample indicates:

Accepted Answer

The sample differs significantly from the distribution the autoencoder was trained on, suggesting an anomaly

Answer

Autoencoders trained on normal data learn to reconstruct normal patterns well; anomalous inputs lie off-manifold and result in high reconstruction error.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Unsupervised Machine Learning Models 5