In SVM, what does the term 'support vectors' refer to?
Practice Questions
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Q1
In SVM, what does the term 'support vectors' refer to?
Data points that are farthest from the decision boundary
Data points that lie on the decision boundary
All data points in the dataset
Data points that are misclassified
Support vectors are the data points that lie closest to the decision boundary and are critical in defining the position and orientation of the boundary.
Questions & Step-by-step Solutions
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Q
Q: In SVM, what does the term 'support vectors' refer to?
Solution: Support vectors are the data points that lie closest to the decision boundary and are critical in defining the position and orientation of the boundary.
Steps: 5
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, a decision boundary is a line (or hyperplane) that separates different classes of data points.
Step 3: Support vectors are specific data points that are closest to this decision boundary.
Step 4: These support vectors are important because they help determine where the decision boundary is placed.
Step 5: If you remove a support vector, the position of the decision boundary may change, but removing other points that are not support vectors will not affect it.
