AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

The statement "Clustering algorithms predict categorical class labels" is false. Clustering algorithms primarily group data points into clusters based on their similarities and differences, rather than predicting specific categorical class labels for those data points. The fundamental purpose of clustering is to identify inherent patterns or groupings within the data without pre-defined labels.

These algorithms work by analyzing the features of the data and finding structures that emerge from these features. For example, techniques such as K-means or hierarchical clustering will cluster points based on distance measures, but they do not assign a categorical label to each cluster in the way that classification algorithms do. In contrast, classification algorithms, like decision trees or support vector machines, are designed to predict and assign specific labels based on learned examples.

While some clustering techniques might allow for post-analysis where you could assign labels to the clusters after examining them, this does not mean that the clustering process inherently predicts class labels in the same way classification algorithms do. Therefore, the key distinction lies in the purpose and methodology of clustering versus classification.