MS-DS Master of Data science Unsupervised Learning Techniques Questions and Answers

Question 1

A marketing team wants to segment its customer base into distinct groups based on purchasing behaviors (e.g., frequency, recency, monetary value) but has no predefined group labels for the customers. Which unsupervised learning technique is most suitable for this task?

Accepted Answer

K-Means Clustering

Answer

K-Means Clustering is a partitional clustering algorithm designed to group unlabeled data into a pre-specified number of clusters (K), where each data point belongs to the cluster with the nearest mean. This is ideal for customer segmentation. PCA is for dimensionality reduction, while Linear Regression and SVMs are supervised learning algorithms.

Question 2

What is the primary objective of Principal Component Analysis (PCA) when used as an unsupervised learning technique?

Accepted Answer

To reduce dimensionality by finding a new set of uncorrelated variables (principal components) that capture the maximum variance in the data.

Answer

PCA is a dimensionality reduction technique. Its main goal is to transform a large set of variables into a smaller, more manageable set while retaining as much of the original information (variance) as possible. The new variables, or principal components, are linear combinations of the original variables and are uncorrelated.

Question 3

Which of the following statements accurately describes a key difference between K-Means and DBSCAN clustering algorithms?

Accepted Answer

DBSCAN can identify noise points (outliers) that do not belong to any cluster, whereas K-Means assigns every point to a cluster.

Answer

A significant advantage of DBSCAN is its ability to handle noise and identify outliers. Points in low-density regions are marked as noise and do not get assigned to any cluster. In contrast, K-Means forces every single data point into one of the K clusters, which can distort the cluster shapes if outliers are present.

Question 4

A financial institution is analyzing a large dataset of credit card transactions to identify fraudulent activities. These fraudulent transactions are rare and often exhibit unusual patterns compared to the vast majority of legitimate transactions. Which type of unsupervised learning is most appropriate for this specific task?

Accepted Answer

Anomaly Detection

Answer

Anomaly detection (or outlier detection) is the branch of unsupervised learning focused on identifying rare items, events, or observations that differ significantly from the majority of the data. Since fraudulent transactions are rare and deviate from normal patterns, this is the most suitable approach.

Question 5

In agglomerative hierarchical clustering, what does the 'linkage criterion' (e.g., single, complete, average) determine?

Accepted Answer

The method for measuring the distance between clusters to decide which ones to merge next.

Answer

The linkage criterion defines how the distance between two clusters is calculated at each step of the agglomerative process. For example, 'complete' linkage uses the maximum distance between points in the two clusters, 'single' linkage uses the minimum distance, and 'average' uses the average distance. This criterion is crucial for determining the merge order and the final hierarchy.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Unsupervised Learning Techniques Questions and Answers