Support Vector Machine Flashcards

1
Q

Support Vector Machine

A

To establish the broadest possible “street” between distinct classes.

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2
Q

Support vector

A

Any instance located on the “street”.

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3
Q

Decision boundary

A

Entirely determined by the support vectors

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4
Q

2 Type of Support Vector Machine

A

SVM Classification - Fit the largest possible street between two classes while limiting margin violation
SVM Regression - Fit as many instances as possible on the street while limiting margin violations

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5
Q

2 Type of Support Vector Machine Margins (Only for classification)

A

Hard Margin Classification
Soft Margin Classification - To find a balance between maintaining the street as wide as possible while also limiting the number of margin violations.

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6
Q

2 Characteristic of Hard Margin Classification

A

All instances must be off the street
All instances must be on the right side

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7
Q

2 Issue of Hard Margin Classification

A

Works only when data is linearly separable.
Sensitive to outliers.

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8
Q

2 Approach of Nonlinear SVM Classification

A

Approach 1 - High polynomial degree
Approach 2 - Polynomial Kernel (kernel trick) - Higher dimensional; Not add new features

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9
Q

C (Regularization Hyperparameter)

A

Controls the trade-off between achieving a large margin and minimising classification errors

Small value of C - Wider margin & some misclassifications (margin violations)
Large value of C - Narrower margin & potentially overfit

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