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Questions and Answers
What is the default behavior of SVM as a classifier?
What is the default behavior of SVM as a classifier?
- It works as a linear classifier (correct)
- It works as a clustering algorithm
- It works as a dimensionality reduction technique
- It works as a non-linear classifier
How is a data set defined as linearly separable in the context of SVM?
How is a data set defined as linearly separable in the context of SVM?
- If it has a non-linear decision boundary
- If it has a high dimensional feature space
- If a hyperplane can separate the data into required classes (correct)
- If it can only be separated by non-linear methods
What is the decision boundary in 3 dimensions for a linearly separable data set?
What is the decision boundary in 3 dimensions for a linearly separable data set?
- A line
- A point
- A plane (correct)
- A hyperplane
In general, what is the separator for a linearly separable data set in d dimensions?
In general, what is the separator for a linearly separable data set in d dimensions?
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What are the primary applications of SVM?
What are the primary applications of SVM?
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Study Notes
SVM Classifier Behavior
- Defaults to a soft margin classifier, allowing for some misclassifications to achieve better overall performance
Linearly Separable Data Set
- A data set is defined as linearly separable if it can be separated by a single hyperplane
Decision Boundary in 3 Dimensions
- The decision boundary for a linearly separable data set in 3 dimensions is a plane that separates the classes
Separator in d Dimensions
- In d dimensions, the separator for a linearly separable data set is a (d-1) dimensional hyperplane that separates the classes
Primary Applications of SVM
- Classification and regression tasks, particularly in text classification, image classification, and bioinformatics
- Handling high-dimensional data and noisy data
- Outperforming traditional machine learning methods in many applications
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