Q. How does SVM handle outliers in the training data?
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A.
By ignoring them completely
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B.
By assigning them a higher weight
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C.
By using a soft margin approach
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D.
By clustering them separately
Solution
SVM can handle outliers using a soft margin approach, which allows some misclassifications to achieve a better overall model.
Correct Answer:
C
— By using a soft margin approach
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Q. In the context of SVM, what does the term 'margin' refer to?
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A.
The distance between the closest data points of different classes
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B.
The area where no data points exist
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C.
The total number of support vectors
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D.
The error rate of the model
Solution
The margin refers to the distance between the closest data points of different classes, which SVM aims to maximize.
Correct Answer:
A
— The distance between the closest data points of different classes
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Q. In which real-world application is SVM particularly effective?
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A.
Image recognition
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B.
Time series forecasting
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C.
Natural language processing
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D.
Reinforcement learning
Solution
SVM is particularly effective in image recognition tasks due to its ability to handle high-dimensional data and create complex decision boundaries.
Correct Answer:
A
— Image recognition
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Q. What is the effect of increasing the regularization parameter (C) in SVM?
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A.
Increases the margin width
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B.
Decreases the margin width
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C.
Increases the number of support vectors
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D.
Decreases the number of support vectors
Solution
Increasing the regularization parameter (C) decreases the margin width, allowing the model to fit the training data more closely.
Correct Answer:
B
— Decreases the margin width
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Q. What is the primary advantage of using SVM for classification tasks?
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A.
It is computationally inexpensive
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B.
It can handle high-dimensional spaces effectively
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C.
It requires less training data
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D.
It is always interpretable
Solution
SVM can handle high-dimensional spaces effectively, making it suitable for complex classification tasks.
Correct Answer:
B
— It can handle high-dimensional spaces effectively
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Q. What is the purpose of the 'gamma' parameter in the RBF kernel of SVM?
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A.
To control the width of the margin
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B.
To define the influence of a single training example
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C.
To adjust the number of support vectors
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D.
To increase the dimensionality of the data
Solution
The 'gamma' parameter in the RBF kernel controls the influence of a single training example, affecting the decision boundary's shape.
Correct Answer:
B
— To define the influence of a single training example
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Q. What is the role of the hyperplane in SVM?
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A.
To cluster the data points
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B.
To separate different classes
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C.
To reduce dimensionality
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D.
To calculate the loss function
Solution
The hyperplane in SVM serves to separate different classes in the feature space.
Correct Answer:
B
— To separate different classes
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Q. Which evaluation metric is most appropriate for assessing the performance of an SVM model on an imbalanced dataset?
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A.
Accuracy
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B.
Precision
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C.
Recall
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D.
F1 Score
Solution
The F1 Score is a better evaluation metric for imbalanced datasets as it considers both precision and recall.
Correct Answer:
D
— F1 Score
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Q. Which evaluation metric is most appropriate for assessing the performance of an SVM model on imbalanced datasets?
-
A.
Accuracy
-
B.
Precision
-
C.
Recall
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D.
F1 Score
Solution
The F1 Score is a better evaluation metric for imbalanced datasets as it considers both precision and recall, providing a balance between the two.
Correct Answer:
D
— F1 Score
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Q. Which of the following is a disadvantage of using SVM?
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A.
It can handle large datasets efficiently
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B.
It is sensitive to the choice of kernel
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C.
It provides probabilistic outputs
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D.
It is easy to interpret
Solution
SVM is sensitive to the choice of kernel, which can significantly affect its performance and requires careful tuning.
Correct Answer:
B
— It is sensitive to the choice of kernel
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