Q. In a binary classification problem using SVM, what does a decision boundary represent?
A.
The line that separates the two classes
B.
The average of all data points
C.
The centroid of the data points
D.
The area of overlap between classes
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Solution
The decision boundary in SVM represents the hyperplane that separates the two classes in the feature space.
Correct Answer:
A
— The line that separates the two classes
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Q. What does the term 'margin' refer to in the context of SVM?
A.
The distance between the closest data points of different classes
B.
The total number of support vectors
C.
The area under the ROC curve
D.
The error rate of the model
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Solution
In SVM, the margin is the distance between the closest data points of different classes, which the algorithm aims to maximize.
Correct Answer:
A
— The distance between the closest data points of different classes
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Q. Which of the following is a common evaluation metric for SVM classification performance?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
Confusion Matrix
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Solution
Accuracy is a common evaluation metric used to assess the performance of SVM classifiers by measuring the proportion of correctly classified instances.
Correct Answer:
B
— Accuracy
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Q. Which of the following scenarios is best suited for using SVM?
A.
When the dataset is small and linearly separable
B.
When the dataset is large and contains many outliers
C.
When the dataset is high-dimensional with clear margins of separation
D.
When the dataset is unstructured and requires clustering
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Solution
SVM performs well in high-dimensional spaces and is effective when there are clear margins of separation between classes.
Correct Answer:
C
— When the dataset is high-dimensional with clear margins of separation
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