Machine Learning: Support Vector Machines

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Questions and Answers

What is the primary purpose of Support Vector Machines in machine learning?

To reduce the dimensionality of the dataset
To find the best hyperplane for classification and regression tasks (correct)
To improve the accuracy of decision trees
To cluster similar data points

What is the goal of finding the best hyperplane in SVMs?

To maximize the margin between different data classes (correct)
To reduce the number of data points
To find the shortest distance between data points
To minimize the distance between data points

What are the data points closest to the hyperplane called?

Hyperplane points
Decision boundaries
Data clusters
Support vectors (correct)

What is the main objective of learning a Linear SVM?

To maximize the margin (D) Signup and view all the answers

What is the main difference between B1 and B2 in the given diagrams?

B1 has a smaller margin, while B2 has a larger margin (D) Signup and view all the answers

What are the values of f(x) in a Linear SVM?

1, -1 (B) Signup and view all the answers

What is the criteria to determine which hyperplane is better?

The hyperplane with the largest margin (C) Signup and view all the answers

What is the term for the data classes being separated by a hyperplane?

Linearly separable (B) Signup and view all the answers

What is the decision boundary in a Linear SVM?

w*x + b = 0 (C) Signup and view all the answers

What are the constraints in learning a Linear SVM?

yi = 1 if wx_i + b >= 1, yi = -1 if wx_i + b <= -1 (B) Signup and view all the answers

What is a good decision boundary?

A boundary that maximizes the margin (D) Signup and view all the answers

Why are all decision boundaries not equally good?

Because some boundaries may not maximize the margin (C) Signup and view all the answers

What is the significance of the margin in a Linear SVM?

It is the minimum distance between the decision boundary and the data points (A) Signup and view all the answers

What is the role of w and b in a Linear SVM?

w is the weight vector and b is the bias term (A) Signup and view all the answers

What is the main advantage of SVM in terms of outliers?

It is robust to outliers (B) Signup and view all the answers

What is the purpose of the kernel trick in SVM?

To transform a low-dimensional input space into a higher-dimensional space (D) Signup and view all the answers

What type of separation problem is the kernel trick mostly useful for?

Non-linear separation problems (D) Signup and view all the answers

What is the main challenge in scenario-4?

Handling outliers (D) Signup and view all the answers

What is the main advantage of using SVM in scenario-5?

It can handle non-linear separation problems (D) Signup and view all the answers

What is the purpose of the hyperplane in SVM?

To find the maximum margin between classes (A) Signup and view all the answers

What is an example of a kernel function used in SVM?

Polynomial kernel (A) Signup and view all the answers

What is the main benefit of using the kernel trick in SVM?

It converts a non-separable problem into a separable problem (A) Signup and view all the answers

What is the primary goal of Support Vector Machines (SVMs)?

Classifying the classes accurately prior to maximizing margin (D) Signup and view all the answers

In Scenario-2, which hyperplane is selected as the right hyperplane?

Hyperplane C (C) Signup and view all the answers

What is the consequence of selecting a hyperplane with a low margin?

Higher chance of misclassification (C) Signup and view all the answers

In Scenario-1, which hyperplane is used to classify star and circle?

Hyperplane B (D) Signup and view all the answers

What is the term used to describe the distance between the hyperplane and the nearest data points?

Margin (C) Signup and view all the answers

Why is hyperplane A chosen in Scenario-3?

It classifies the classes accurately without errors (B) Signup and view all the answers

What is the incorrect assumption in Scenario-3?

SVM selects the hyperplane with the highest margin (A) Signup and view all the answers

What is the main difference between Scenario-1 and Scenario-2?

The classification accuracy of the hyperplanes (A) Signup and view all the answers

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Study Notes

Support Vector Machines (SVMs)

SVM is a powerful algorithm in machine learning used for both classification (binary) and regression tasks
SVMs work by finding the best hyperplane (a line in higher dimensions) that separates different data classes with the maximum margin (linearly separable)

Finding the Best Hyperplane

The goal is to find a linear hyperplane (decision boundary) that will separate the data
Multiple possible solutions exist, but the best hyperplane is the one that maximizes the margin

Margin and Hyperplane Selection

The margin is the distance between the hyperplane and the closest data points (support vectors)
The hyperplane with the maximum margin is the best choice
If multiple hyperplanes have the same maximum margin, the one with the lowest classification error is selected

Linear SVM

The linear model is defined by the equation: w x + b = 0
The goal is to determine the values of w and b
The learning objective is to maximize the margin, subject to constraints on the data points

Examples of Bad Decision Boundaries

Multiple decision boundaries may exist, but not all are equally good
A good decision boundary is one that maximizes the margin and minimizes classification errors

Identifying the Right Hyperplane

In scenario 1, hyperplane B is the best choice because it separates the classes with the maximum margin
In scenario 2, hyperplane C is the best choice because it has the highest margin
In scenario 3, hyperplane A is the best choice because it classifies the classes accurately, even though hyperplane B has a higher margin

Handling Non-Linearly Separable Data

SVM has a feature to ignore outliers and find the hyperplane that has maximum margin
SVM is robust to outliers
The kernel trick is a technique used to convert non-separable problems into separable problems
Kernels are functions that take a low-dimensional input space and transform it into a higher-dimensional space
Examples of kernels include polynomial kernel, Gaussian kernel, and sigmoid kernel

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