Support vectors are data points near a hyperplane that affect the position and orientation of the hyperplane. Use these support vectors to maximize the classifier margin. Deleting support vectors changes the position of the hyperplane. These are the phases that help create the SVM. With a regularization parameter of 1, the SVM uses 81 support vectors to classify the flowers in the iris dataset with an accuracy of 0.82. These training instances can be viewed as "supporting" or "maintaining" the optimal hyperplane. That is the reason they are "support vectors". These training instances can be viewed as "supporting" or "maintaining" the optimal hyperplane.

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BY Best Interview Question ON 30 Dec 2023