Support Vector Machine: Maximal Margin Classifier

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Questions and Answers

What is the key characteristic of a maximal margin classifier?

Enriching and enlarging the feature space
Linearly separable classes only (correct)
Default values for separating line
Support Vector Machine capabilities

In which scenario does the Support Vector Classifier (SVC) operate?

Only with linearly separable classes
Linear, 'soft separate' approach (correct)
Default values for separating line
Enriching and enlarging the feature space

What is the primary purpose of enlarging the feature space in Support Vector Machines?

To default values for separating line
To minimize the number of support vectors
To improve prediction accuracy (correct)
To maximize the margin between classes

What is the default value used for separating lines in the context of maximal margin classifiers?

-1 (B) Signup and view all the answers

In maximal margin classifiers, what does the margin refer to?

The distance between classes' decision boundaries (D) Signup and view all the answers

How do 2-D maximal margin classifiers separate classes in multiple dimensions?

By defining hyperplanes to divide classes (D) Signup and view all the answers

What is the main characteristic of the Maximal Margin Classifier?

It perfectly separates the two classes (B) Signup and view all the answers

In Support Vector Classifier (SVC), what role do support vectors play?

They determine the classifier on the margin or violate the margin (D) Signup and view all the answers

What does a tuning parameter 'C' of 0 indicate in a Support Vector Classifier (SVC)?

Zero tolerance for misclassifications (A) Signup and view all the answers

How do observations that lie on the correct side of the margin affect Support Vector Classifier (SVC)?

They do not affect SVC at all (B) Signup and view all the answers

What kind of error is minimized by the Maximal Margin Classifier?

Combination of margin and misclassification errors (B) Signup and view all the answers

Why are observations that lie on the margin or on the wrong side of the margin considered as support vectors?

Because they violate the margin (A) Signup and view all the answers

What is the main idea behind the construction of the Maximal Margin Classifier?

To correctly classify observations with the maximum possible margin between the classes (A) Signup and view all the answers

What role do support vectors play in a Support Vector Classifier (SVC)?

Support vectors are data points closest to the hyperplane and influence its position (A) Signup and view all the answers

Why is it important for a separating hyperplane to have a large margin in a Maximal Margin Classifier?

Large margins lead to better generalization and improved classifier performance (B) Signup and view all the answers

Which library and function can be used to implement a Maximal Margin Classifier in R?

Solution Library(e1071) svm() (D) Signup and view all the answers

How does a separating hyperplane divide a p-dimensional space in relation to the training data?

It splits the space into two halves while separating the training data (B) Signup and view all the answers

What is a key characteristic of the General format of a hyperplane used in Margin Classifiers?

It can accommodate non-linear decision boundaries through kernel tricks (A) Signup and view all the answers

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Study Notes

Maximal Margin Classifier

A type of supervised non-probabilistic binary classifier that only works with linearly separable classes.
Aims to find the hyperplane that maximizes the margin between two classes.

Support Vector Classifier

An extension of the Maximal Margin Classifier that can handle linearly separable and non-linearly separable classes.
Enriches and enlarges the feature space to make separation possible.

Support Vector Machine (SVM)

A type of supervised non-probabilistic binary classifier that can handle both linear and non-linear classes.
Enriches and enlarges the feature space to make separation possible.

Separating Line for Two Classes

The decision boundary for separating two classes.
Example: Credit Score vs. Default/Not Default.

Maximal Margin Classifier (MMC)

Aims to find the hyperplane with the largest margin that separates the two classes.
The hyperplane is dependent on only a few observations, which are called support vectors.

Support Vectors

Observations that determine the classifier on the margin or violate the margin.
Characteristics:
- Observations that lie on the correct side of the margin do not affect the SVM.
- Observations that lie on the margin, or on the wrong side of the margin are support vectors.

Soft Margins

Allow for misclassifications by introducing a tolerance parameter C.
C specifies the allowance for misclassifications:
- C = 0: zero tolerance (Maximal Margin Classifier).
- C > 0: some tolerance for margin violations and misclassifications.

R Implementation

Use the e1071 library and the svmfit function.
Set the tuning parameter C to specify the tolerance for misclassifications.

Characteristics of SVC

Observations that lie on the correct side of the margin do not affect the SVM.
Observations that lie on the margin, or on the wrong side of the margin are support vectors.

Hyperplanes in p-dimensional space

A hyperplane divides p-dimensional space into two halves.
A separating hyperplane separates the training data.
Multiple or even an infinite number of separating hyperplanes/margin classifiers can exist for a training dataset.

Construction of the Maximal Margin Classifier

Subject to the constraints that each observation is on the correct side of the hyperplane with at least a distance M.
Solution: Use the svm() function from the e1071 library.

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