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In SVM, what are support vectors?

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Question: In SVM, what are support vectors?

Options:

  1. Data points that are farthest from the decision boundary
  2. Data points that lie on the decision boundary
  3. Data points that are misclassified
  4. All data points in the dataset

Correct Answer: Data points that lie on the decision boundary

Solution: 

Support vectors are the data points that lie closest to the decision boundary and are critical in defining the position of the hyperplane.

In SVM, what are support vectors?

Practice Questions

Q1
In SVM, what are support vectors?
  1. Data points that are farthest from the decision boundary
  2. Data points that lie on the decision boundary
  3. Data points that are misclassified
  4. All data points in the dataset

Questions & Step-by-Step Solutions

In SVM, what are support vectors?
  • Step 1: Understand that SVM stands for Support Vector Machine, which is a type of machine learning algorithm used for classification tasks.
  • Step 2: In SVM, we try to separate different classes of data points using a line (in 2D) or a hyperplane (in higher dimensions).
  • Step 3: The decision boundary is the line or hyperplane that separates the different classes.
  • Step 4: Support vectors are the specific data points that are closest to this decision boundary.
  • Step 5: These support vectors are important because they help determine the position and orientation of the decision boundary.
  • Step 6: If we remove a support vector, the decision boundary may change, but removing other points that are not support vectors will not affect it.
  • Support Vectors – Support vectors are the data points that are closest to the decision boundary in SVM and are essential for determining the optimal hyperplane.
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