Data Science Unsupervised Learning Techniques Questions and Answers

Question 1

A data science team is using the K-Means algorithm for customer segmentation. They notice that running the algorithm multiple times on the same dataset produces slightly different final clusters. What is the most likely cause of this inconsistent output?

Accepted Answer

K-Means is highly sensitive to the initial random placement of cluster centroids.

Answer

The K-Means algorithm begins by randomly initializing the cluster centroids. Depending on these starting positions, the algorithm can converge to different local optima, resulting in different final cluster assignments. This is a well-known characteristic of the algorithm, and a common mitigation strategy is to run it multiple times with different random initializations and select the best result.

Question 2

Which of the following best describes the primary objective of Principal Component Analysis (PCA)?

Accepted Answer

To transform a set of potentially correlated variables into a smaller set of uncorrelated variables while preserving the maximum amount of variance.

Answer

The main goal of PCA is dimensionality reduction. It achieves this by finding a new set of variables, the principal components, which are linear combinations of the original variables. These components are orthogonal (uncorrelated) and are ordered such that the first few retain most of the variance present in the original dataset.

Question 3

A data scientist needs to analyze customer purchase behavior but does not know the optimal number of customer segments beforehand. They want to visualize the relationships and hierarchy between different potential segments. Which unsupervised learning technique is most suitable for this exploratory task?

Accepted Answer

Agglomerative Hierarchical Clustering

Answer

Agglomerative Hierarchical Clustering is ideal for this scenario because it does not require the number of clusters to be specified in advance. It builds a tree-like structure of clusters called a dendrogram, which allows the data scientist to visualize how clusters are merged at different levels and decide on an appropriate number of segments after the analysis.

Question 4

In a retail dataset, the association rule {Diapers} -> {Beer} has a confidence of 0.8. What is the correct interpretation of this value?

Accepted Answer

Of all the transactions that contain Diapers, 80% also contain Beer.

Answer

Confidence in association rule mining measures the conditional probability of the consequent given the antecedent. A confidence of 0.8 for the rule {Diapers} -> {Beer} means that in 80% of the transactions where Diapers were purchased, Beer was also purchased.

Question 5

A data analyst is tasked with segmenting a customer dataset that contains clusters of non-spherical shapes and a significant number of outliers. Which clustering algorithm would be the most effective choice in this situation?

Accepted Answer

DBSCAN

Answer

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is specifically designed to handle clusters of arbitrary shapes and is robust to outliers, which it classifies as noise. K-Means assumes clusters are spherical and is sensitive to outliers, making it less suitable for this scenario.

Data Science Practice Test

Data Science Practice Test

Data Science Unsupervised Learning Techniques Questions and Answers