Q. In SVM, what are support vectors?
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A.
Data points that are farthest from the decision boundary
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B.
Data points that lie on the decision boundary
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C.
Data points that are misclassified
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D.
All data points in the dataset
Solution
Support vectors are the data points that lie closest to the decision boundary and are critical in defining the position of the hyperplane.
Correct Answer:
B
— Data points that lie on the decision boundary
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Q. In which scenario would you prefer using SVM over other classification algorithms?
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A.
When the dataset is very large
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B.
When the data is linearly separable
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C.
When the data has a high dimensionality
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D.
When the data is highly imbalanced
Solution
SVM is particularly effective in high-dimensional spaces, making it suitable for datasets with many features.
Correct Answer:
C
— When the data has a high dimensionality
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Q. What does the parameter 'C' control in SVM?
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A.
The complexity of the model
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B.
The margin width
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C.
The number of support vectors
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D.
The learning rate
Solution
'C' is a regularization parameter that controls the trade-off between maximizing the margin and minimizing classification error.
Correct Answer:
A
— The complexity of the model
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Q. What is the effect of using a soft margin in SVM?
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A.
It allows some misclassifications
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B.
It increases the model complexity
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C.
It reduces the number of support vectors
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D.
It guarantees a perfect classification
Solution
A soft margin allows for some misclassifications, which can help in achieving better generalization on unseen data.
Correct Answer:
A
— It allows some misclassifications
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Q. What is the main advantage of using SVM for classification tasks?
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A.
It is computationally inexpensive
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B.
It can handle non-linear relationships
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C.
It requires less data for training
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D.
It is easy to interpret
Solution
SVM can effectively handle non-linear relationships through the use of kernel functions, making it versatile for various datasets.
Correct Answer:
B
— It can handle non-linear relationships
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Q. What is the primary purpose of a Support Vector Machine (SVM)?
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A.
To perform regression analysis
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B.
To classify data into different categories
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C.
To reduce dimensionality of data
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D.
To cluster similar data points
Solution
SVM is primarily used for classification tasks, where it separates data into different categories using hyperplanes.
Correct Answer:
B
— To classify data into different categories
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Q. What is the role of the kernel function in SVM?
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A.
To increase the number of features
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B.
To transform data into a higher-dimensional space
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C.
To reduce overfitting
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D.
To normalize the data
Solution
The kernel function allows SVM to operate in a higher-dimensional space, enabling it to find non-linear decision boundaries.
Correct Answer:
B
— To transform data into a higher-dimensional space
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Q. What type of learning does SVM primarily fall under?
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A.
Supervised learning
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B.
Unsupervised learning
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C.
Reinforcement learning
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D.
Semi-supervised learning
Solution
SVM is a supervised learning algorithm, as it requires labeled data for training.
Correct Answer:
A
— Supervised learning
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Q. Which evaluation metric is commonly used to assess the performance of an SVM model?
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A.
Mean Squared Error
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B.
Accuracy
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C.
Silhouette Score
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D.
Confusion Matrix
Solution
Accuracy is a common metric used to evaluate the performance of classification models, including SVM.
Correct Answer:
B
— Accuracy
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Q. Which of the following is NOT a common kernel used in SVM?
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A.
Linear kernel
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B.
Polynomial kernel
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C.
Radial basis function (RBF) kernel
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D.
Logistic kernel
Solution
Logistic kernel is not a standard kernel used in SVM; common kernels include linear, polynomial, and RBF.
Correct Answer:
D
— Logistic kernel
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Q. Which of the following statements about SVM is true?
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A.
SVM can only be used for binary classification
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B.
SVM is sensitive to outliers
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C.
SVM does not require feature scaling
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D.
SVM is a type of unsupervised learning
Solution
SVM is sensitive to outliers, as they can affect the position of the decision boundary.
Correct Answer:
B
— SVM is sensitive to outliers
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