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Questions and Answers
What does an Inner Product measure?
What does an Inner Product measure?
- Similarity of two observations using standard correlation (correct)
- The magnitude of two vectors
- Differences between two observations
- Efficiency of computational approach
What does a Kernel quantify?
What does a Kernel quantify?
- Differences between two observations
- Efficiency of computational approach
- Magnitude of two vectors
- Similarity of two points (correct)
What happens when the degree in a polynomial kernel is set to 1?
What happens when the degree in a polynomial kernel is set to 1?
- Efficient computational approach is achieved
- Radial Kernel is used
- SVMFit reduces to the support vector classifier (correct)
- Support Vector Machine transforms data into higher dimension space
Which type of kernel operates in infinite dimensions?
Which type of kernel operates in infinite dimensions?
What is the purpose of a Support Vector Classifier with Non-Linear Kernels?
What is the purpose of a Support Vector Classifier with Non-Linear Kernels?
What does < Vector D1 , Vector D2 > represent?
What does < Vector D1 , Vector D2 > represent?
In SVM, what does a larger gamma parameter value imply for the radial kernel?
In SVM, what does a larger gamma parameter value imply for the radial kernel?
What is the effect of having a smaller cost parameter in SVM?
What is the effect of having a smaller cost parameter in SVM?
How does SVM handle the process of tuning hyperparameters?
How does SVM handle the process of tuning hyperparameters?
What does a positive value of the cost parameter in SVM indicate?
What does a positive value of the cost parameter in SVM indicate?
How does SVM approach the classification task with the radial kernel?
How does SVM approach the classification task with the radial kernel?
What is a key role of the gamma parameter in SVM with the radial kernel?
What is a key role of the gamma parameter in SVM with the radial kernel?
What is the main limitation of the Support Vector Classifier (SVC) according to the text?
What is the main limitation of the Support Vector Classifier (SVC) according to the text?
How can the limitations of the Support Vector Classifier (SVC) be addressed?
How can the limitations of the Support Vector Classifier (SVC) be addressed?
What is the purpose of transforming lower dimension data into a higher dimension space in Support Vector Machine (SVM)?
What is the purpose of transforming lower dimension data into a higher dimension space in Support Vector Machine (SVM)?
Why is Linear SVC considered inadequate for dealing with Mortgage $ and Age features?
Why is Linear SVC considered inadequate for dealing with Mortgage $ and Age features?
What does Support Vector Machine (SVM) do to handle non-linear classes?
What does Support Vector Machine (SVM) do to handle non-linear classes?
Why is it necessary to enlarge the feature space in Support Vector Machine (SVM)?
Why is it necessary to enlarge the feature space in Support Vector Machine (SVM)?
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