Support Vector Machines: Linear vs Non-linear Classes Quiz
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Questions and Answers

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?

  • 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?

  • 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?

    <p>Radial Kernel</p> Signup and view all the answers

    What is the purpose of a Support Vector Classifier with Non-Linear Kernels?

    <p>Transform lower dimension data into higher dimension space</p> Signup and view all the answers

    What does < Vector D1 , Vector D2 > represent?

    <p>| D1 | * | D2 | * cos(θ)</p> Signup and view all the answers

    In SVM, what does a larger gamma parameter value imply for the radial kernel?

    <p>Smaller kernel</p> Signup and view all the answers

    What is the effect of having a smaller cost parameter in SVM?

    <p>Wider kernel</p> Signup and view all the answers

    How does SVM handle the process of tuning hyperparameters?

    <p>Through the tune() function</p> Signup and view all the answers

    What does a positive value of the cost parameter in SVM indicate?

    <p>Higher complexity</p> Signup and view all the answers

    How does SVM approach the classification task with the radial kernel?

    <p>By minimizing misclassifications</p> Signup and view all the answers

    What is a key role of the gamma parameter in SVM with the radial kernel?

    <p>Adjusting class boundaries</p> Signup and view all the answers

    What is the main limitation of the Support Vector Classifier (SVC) according to the text?

    <p>It is only suitable for linearly separable classes</p> Signup and view all the answers

    How can the limitations of the Support Vector Classifier (SVC) be addressed?

    <p>By transforming data into a higher dimension space using kernels</p> Signup and view all the answers

    What is the purpose of transforming lower dimension data into a higher dimension space in Support Vector Machine (SVM)?

    <p>To increase the feature space for better separation</p> Signup and view all the answers

    Why is Linear SVC considered inadequate for dealing with Mortgage $ and Age features?

    <p>Mortgage $ and Age are not linearly separable</p> Signup and view all the answers

    What does Support Vector Machine (SVM) do to handle non-linear classes?

    <p>It enlarges the feature space using kernels</p> Signup and view all the answers

    Why is it necessary to enlarge the feature space in Support Vector Machine (SVM)?

    <p>To enable better separation of classes</p> Signup and view all the answers

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