Data Science with Python Unsupervised Learning Algorithms Questions and Answers

Question 1

A retail company wants to segment its customer base into distinct groups based on purchasing behavior (e.g., purchase frequency, monetary value, items purchased) to tailor marketing campaigns.
The company does not have any predefined labels for these customer groups.
Which of the following algorithms is most appropriate for this task?

Accepted Answer

K-Means Clustering

Answer

Logistic Regression

Answer

Linear Regression

Answer

A/B Testing

Question 2

What is the primary objective of the Principal Component Analysis (PCA) algorithm?

Accepted Answer

To reduce the dimensionality of a dataset while preserving the maximum amount of variance.

Answer

To group similar data points into a specified number of clusters.

Answer

To predict a continuous target variable based on one or more predictor variables.

Answer

To find the optimal set of hyperparameters for a machine learning model.

Question 3

A data scientist is using the K-Means algorithm and needs to configure its parameters. Which of the following is a mandatory hyperparameter that must be specified *before* running the algorithm?

Accepted Answer

The number of clusters (K)

Answer

The epsilon (ε) neighborhood distance

Answer

The linkage criterion

Answer

The number of principal components

Question 4

A cybersecurity analyst is processing a large dataset of network traffic to identify novel, previously unseen types of attacks (zero-day exploits). These attacks would appear as unusual patterns distinct from the vast amount of normal traffic. Which unsupervised learning task is most suitable for this scenario?

Accepted Answer

Anomaly Detection

Answer

Dimensionality Reduction

Answer

Association Rule Mining

Answer

Regression

Question 5

Which of the following clustering algorithms is particularly well-suited for discovering clusters of arbitrary shapes and is robust to outliers, which it can identify as noise?

Accepted Answer

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

Answer

K-Means

Answer

Principal Component Analysis (PCA)

Answer

Linear Regression

Question 6

In agglomerative hierarchical clustering, what is the purpose of the 'linkage criterion' (e.g., 'ward', 'complete', 'average')?

Accepted Answer

To define how the distance between two clusters is measured in order to decide which clusters to merge.

Answer

To determine the initial number of clusters.

Answer

To specify the distance metric used to calculate the space between individual data points.

Answer

To set the threshold for cutting the dendrogram to form the final clusters.