What is the primary goal of a Support Vector Machine (SVM) in classification tasks?

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BlackTom AI · Accepted Answer

We start by considering what an SVM is designed to do in classification tasks.
Option 1: 'To cluster data points into groups without using labels' describes unsupervised clustering, not supervised classification. SVMs rely on labeled training data to learn a decision boundary, so this is incorrect.
Option 2: 'To randomly assign class labels based on data distribution' suggests randomness rather than a principled learning process. SVM aims to optimize a boundary based on the data and labels, not random labeling, so this is incorrect.
Option 3: 'To find the hyperplane (line) that best separates the classes with the maximum margin' captures the fundamental objective of an SVM in classification: identify a decision boundary (hyperplane) that maximizes the margin between the classes, using support vectors to define that boundary. This statement aligns with the core theory of SVMs, making it correct.
Option 4: 'To calculate the average distance between data points' is not the goal of SVMs. While distances to the decision boundary matter, the objective is not to compute an average pairwise distance, but to maximize the margin of separation with a separating hyperplane.

IS 4487-006 Fall 2025 Week 12 - Comprehension Quiz

What is the primary goal of a Support Vector Machine (SVM) in classification tasks?

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