Support Vector Machines: Linear vs Non-linear Classes Quiz

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18 Questions

What does an Inner Product measure?

Similarity of two observations using standard correlation

What does a Kernel quantify?

Similarity of two points

What happens when the degree in a polynomial kernel is set to 1?

SVMFit reduces to the support vector classifier

Which type of kernel operates in infinite dimensions?

Radial Kernel

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

Transform lower dimension data into higher dimension space

What does < Vector D1 , Vector D2 > represent?

| D1 | * | D2 | * cos(θ)

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

Smaller kernel

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

Wider kernel

How does SVM handle the process of tuning hyperparameters?

Through the tune() function

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

Higher complexity

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

By minimizing misclassifications

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

Adjusting class boundaries

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

It is only suitable for linearly separable classes

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

By transforming data into a higher dimension space using kernels

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

To increase the feature space for better separation

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

Mortgage $ and Age are not linearly separable

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

It enlarges the feature space using kernels

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

To enable better separation of classes

Test your knowledge on Support Vector Machines, including the concepts of Maximal Margin Classifier, Support Vector Classifier, and Support Vector Machine for both linear and non-linear classes. Explore the use of SVM in supervised non-probabilistic binary classification tasks.

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