Machine Learning Unsupervised Learning 4 — Questions and Answers
Question 1: What is the role of the 'epsilon' parameter in DBSCAN?
- It defines the maximum radius of a neighborhood around a point (Correct answer)
- It sets the minimum number of clusters
- It controls the learning rate
- It specifies the maximum number of iterations
Correct answer: It defines the maximum radius of a neighborhood around a point
Epsilon (ε) defines the radius within which DBSCAN searches for neighboring points to determine core points and cluster membership.
Question 2: Which technique can reduce dimensionality while also revealing nonlinear structure in data?
- UMAP (Correct answer)
- PCA
- Linear Discriminant Analysis
- Factor Analysis
Correct answer: UMAP
UMAP (Uniform Manifold Approximation and Projection) is a nonlinear dimensionality reduction technique that preserves both local and some global structure.
Question 3: What is a key difference between agglomerative and divisive hierarchical clustering?
- Agglomerative starts with each point as its own cluster and merges; divisive starts with one cluster and splits (Correct answer)
- Agglomerative requires K to be specified; divisive does not
- Agglomerative uses density; divisive uses centroids
- Agglomerative produces soft assignments; divisive produces hard assignments
Correct answer: Agglomerative starts with each point as its own cluster and merges; divisive starts with one cluster and splits
Agglomerative (bottom-up) clustering merges the closest clusters iteratively, while divisive (top-down) starts with all data in one cluster and recursively splits.
Question 4: In the context of anomaly detection, which unsupervised approach assigns anomaly scores based on how easily a point can be isolated?
- Isolation Forest (Correct answer)
- One-Class SVM
- K-Means with distance threshold
- DBSCAN noise labeling
Correct answer: Isolation Forest
Isolation Forest isolates observations by randomly partitioning data; anomalies are isolated in fewer steps (shorter path lengths) than normal points.
Question 5: What is the 'reconstruction error' in an autoencoder used as a signal for?
- Detecting anomalies, since unusual inputs have higher reconstruction error (Correct answer)
- Measuring classification accuracy
- Selecting the optimal number of latent dimensions
- Evaluating cluster quality
Correct answer: Detecting anomalies, since unusual inputs have higher reconstruction error
Autoencoders trained on normal data learn to reconstruct normal inputs well; anomalous inputs result in higher reconstruction error, flagging them as outliers.
Question 6: Which of the following best describes 'feature learning' in unsupervised learning?
- Automatically discovering useful representations from raw data without labels (Correct answer)
- Selecting the most important supervised features
- Engineering handcrafted features from domain knowledge
- Removing correlated features before classification
Correct answer: Automatically discovering useful representations from raw data without labels
Feature learning (representation learning) involves algorithms like autoencoders and deep belief networks that discover compact, meaningful representations from unlabeled data.
Question 7: What is a Variational Autoencoder (VAE) primarily used for compared to a standard autoencoder?
- Generating new samples by learning a smooth latent space distribution (Correct answer)
- More efficient data compression
- Better anomaly detection accuracy
- Faster training convergence
Correct answer: Generating new samples by learning a smooth latent space distribution
VAEs impose a probabilistic distribution (typically Gaussian) on the latent space, enabling generation of new, realistic samples by sampling from that distribution.
Machine Learning Unsupervised Learning 4
What is the role of the 'epsilon' parameter in DBSCAN?