# 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

### 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)

Smaller kernel

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 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

### 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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