Q. In SVM, what does the term 'support vectors' refer to?
A.
Data points that are farthest from the decision boundary
B.
Data points that lie on the decision boundary
C.
All data points in the dataset
D.
Data points that are misclassified
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Solution
Support vectors are the data points that lie closest to the decision boundary and are critical in defining the position and orientation of the boundary.
Correct Answer:
B
— Data points that lie on the decision boundary
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Q. In the context of SVM, what does 'soft margin' refer to?
A.
A margin that allows some misclassifications
B.
A margin that is strictly enforced
C.
A margin that is not defined
D.
A margin that is only applicable to linear SVM
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Solution
A soft margin allows some misclassifications to achieve a better overall model performance, especially in cases of overlapping classes.
Correct Answer:
A
— A margin that allows some misclassifications
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Q. What is the effect of using a very small value for the regularization parameter 'C' in SVM?
A.
Increased model complexity
B.
Increased margin width
C.
More misclassifications
D.
Decreased training time
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Solution
A very small value for 'C' allows for a wider margin, potentially leading to more misclassifications but better generalization.
Correct Answer:
B
— Increased margin width
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Q. What is the main advantage of using SVM over other classification algorithms?
A.
Simplicity in implementation
B.
Ability to handle large datasets
C.
Robustness to overfitting in high-dimensional spaces
D.
Faster training times
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Solution
SVM is particularly robust to overfitting in high-dimensional spaces due to its margin maximization approach.
Correct Answer:
C
— Robustness to overfitting in high-dimensional spaces
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Q. What is the primary application of SVM in real-world scenarios?
A.
Image classification
B.
Time series forecasting
C.
Clustering
D.
Dimensionality reduction
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Solution
SVM is widely used in image classification tasks due to its effectiveness in high-dimensional spaces.
Correct Answer:
A
— Image classification
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Q. What is the primary goal of a Support Vector Machine (SVM)?
A.
To minimize the error rate
B.
To maximize the margin between classes
C.
To reduce dimensionality
D.
To perform clustering
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Solution
The primary goal of an SVM is to maximize the margin between the classes, which helps in achieving better generalization.
Correct Answer:
B
— To maximize the margin between classes
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Q. What is the role of the regularization parameter 'C' in SVM?
A.
To control the complexity of the model
B.
To determine the type of kernel used
C.
To set the number of support vectors
D.
To adjust the learning rate
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Solution
The regularization parameter 'C' controls the trade-off between maximizing the margin and minimizing the classification error.
Correct Answer:
A
— To control the complexity of the model
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Q. Which evaluation metric is most appropriate for assessing the performance of an SVM model?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
Confusion Matrix
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Solution
Accuracy is a common evaluation metric for classification tasks, including those performed by SVM models.
Correct Answer:
B
— Accuracy
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Q. Which kernel function is commonly used in SVM to handle non-linear data?
A.
Linear kernel
B.
Polynomial kernel
C.
Radial basis function (RBF) kernel
D.
Sigmoid kernel
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Solution
The Radial Basis Function (RBF) kernel is commonly used in SVM to handle non-linear data by mapping it into a higher-dimensional space.
Correct Answer:
C
— Radial basis function (RBF) kernel
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Q. Which of the following applications is well-suited for SVM?
A.
Image classification
B.
Time series forecasting
C.
Text generation
D.
Reinforcement learning
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Solution
SVM is well-suited for image classification tasks due to its effectiveness in high-dimensional spaces.
Correct Answer:
A
— Image classification
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Q. Which of the following is NOT a characteristic of SVM?
A.
Effective in high-dimensional spaces
B.
Memory efficient
C.
Can only be used for binary classification
D.
Uses a margin-based approach
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Solution
SVM can be adapted for multi-class classification using strategies like one-vs-one or one-vs-all, so it is not limited to binary classification.
Correct Answer:
C
— Can only be used for binary classification
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