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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