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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?
- SVM works as a multi-class classifier
- SVM works as a non-linear classifier
- SVM works as a binary classifier
- SVM works as a linear classifier (correct)
In SVM, what is the decision boundary in 2 dimensions?
In SVM, what is the decision boundary in 2 dimensions?
- A line (correct)
- A point
- A curve
- A plane
In SVM, what is the separator in d dimensions?
In SVM, what is the separator in d dimensions?
- A curve
- A line
- A point
- A plane (correct)
What does it mean for a data set to be linearly separable?
What does it mean for a data set to be linearly separable?
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What are the applications of SVM?
What are the applications of SVM?
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Which of the following correctly describes the decision boundary in SVM?
Which of the following correctly describes the decision boundary in SVM?
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What is the default behavior of SVM as a classifier?
What is the default behavior of SVM as a classifier?
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In SVM, what is the separator in d dimensions called?
In SVM, what is the separator in d dimensions called?
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What does it mean for a data set to be linearly separable in SVM?
What does it mean for a data set to be linearly separable in SVM?
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What are the applications of SVM?
What are the applications of SVM?
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Study Notes
SVM Classifier Behavior
- SVM classifiers aim to find the optimal hyperplane that maximizes the margin between classes, ensuring robust classification.
Decision Boundary in 2 Dimensions
- In 2D, the decision boundary in SVM is a line that separates the data points belonging to different classes.
Separator in d Dimensions
- In d dimensions, the separator in SVM is called a hyperplane.
Linearly Separable Data
- A dataset is linearly separable if it's possible to draw a straight line (or a hyperplane in higher dimensions) that completely separates the data points belonging to different classes.
Applications of SVM
- SVMs are widely used in various applications, including:
- Image recognition
- Text classification
- Object detection
- Bioinformatics
Decision Boundary in SVM
- The decision boundary in SVM is the hyperplane that maximizes the margin between the classes.
SVM Default Behavior
- By default, SVM uses a linear kernel, meaning it attempts to find a linear decision boundary to separate the data.
SVM Separator in d Dimensions
- The separator in SVM in d dimensions is called a hyperplane, which is a generalization of a line to higher dimensions.
Linearly Separable Data in SVM
- A dataset is linearly separable in SVM if it can be divided into two classes by a hyperplane.
SVM Applications
- SVMs are widely used in various applications, including:
- Image recognition
- Text classification
- Object detection
- Bioinformatics
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