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

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Which metric is NOT commonly associated with clustering algorithms?

Adjusted Rand Index

Mean Squared Error

Mean Squared Error (MSE) is primarily used in regression analysis to measure the average of the squares of the errors, which is the average squared difference between the estimated values and the actual value. Clustering algorithms, on the other hand, generally focus on grouping data points based on similarities rather than predicting a continuous output variable, making MSE less relevant in this context.

The other metrics mentioned are commonly utilized in clustering evaluation. The Adjusted Rand Index assesses the similarity of two data clusterings, Silhouette Score measures how close an object is to its own cluster compared to other clusters, and the Dunn Index evaluates the ratio of the smallest distance between clusters to the largest intra-cluster distance. All these metrics provide insights specifically into the quality and effectiveness of clustering, reinforcing why Mean Squared Error is not applicable in this scenario.

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Silhouette Score

Dunn Index

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