Data Science Data Science Unsupervised Learning Techniques Questions and Answers 2

Question 1

Which unsupervised learning technique reduces the dimensionality of data by finding principal components that maximize variance?

Accepted Answer

Principal Component Analysis (PCA)

Answer

PCA identifies orthogonal axes (principal components) that capture the maximum variance in the dataset, effectively reducing dimensionality.

Question 2

In DBSCAN clustering, what is a 'core point'?

Accepted Answer

A point with at least minPts neighbors within epsilon distance

Answer

A core point in DBSCAN is defined as a point that has at least minPts data points within its epsilon-radius neighborhood.

Question 3

What does the silhouette coefficient measure in cluster analysis?

Accepted Answer

How similar a point is to its own cluster compared to neighboring clusters

Answer

The silhouette coefficient ranges from -1 to 1 and measures how well each data point fits within its assigned cluster versus the nearest neighboring cluster.

Question 4

Which unsupervised technique is most appropriate for detecting unusual transactions in credit card data without labeled fraud examples?

Accepted Answer

Anomaly detection using Isolation Forest

Answer

Isolation Forest is an unsupervised anomaly detection method that isolates outliers by randomly partitioning data, making it ideal for fraud detection without labels.

Question 5

What is the primary purpose of t-SNE in unsupervised learning?

Accepted Answer

Visualizing high-dimensional data in 2D or 3D space

Answer

t-SNE (t-distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality reduction technique primarily used for visualizing complex high-dimensional datasets.

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Data Science Data Science Unsupervised Learning Techniques Questions and Answers 2