Q. How does SVM handle multi-class classification problems?
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A.
By using a single model for all classes
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B.
By applying one-vs-one or one-vs-all strategies
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C.
By ignoring the additional classes
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D.
By converting them into binary problems only
Solution
SVM can handle multi-class classification using one-vs-one or one-vs-all strategies.
Correct Answer:
B
— By applying one-vs-one or one-vs-all strategies
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Q. In which scenario would you prefer using SVM over logistic regression?
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A.
When the dataset is small
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B.
When the classes are linearly separable
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C.
When the dataset has a high number of features
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D.
When interpretability is crucial
Solution
SVMs are particularly effective in high-dimensional spaces, making them suitable for datasets with many features.
Correct Answer:
C
— When the dataset has a high number of features
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Q. What is a common application of Support Vector Machines (SVM)?
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A.
Image classification
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B.
Time series forecasting
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C.
Reinforcement learning
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D.
Natural language processing
Solution
SVMs are widely used for image classification tasks due to their effectiveness in high-dimensional spaces.
Correct Answer:
A
— Image classification
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Q. What is a common application of SVM in the field of bioinformatics?
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A.
Gene classification
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B.
Weather prediction
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C.
Stock market analysis
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D.
Social media sentiment analysis
Solution
SVMs are often used in bioinformatics for tasks such as gene classification due to their effectiveness in high-dimensional spaces.
Correct Answer:
A
— Gene classification
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Q. What is a common evaluation metric for SVM performance?
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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 SVM classifiers.
Correct Answer:
B
— Accuracy
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Q. What is a primary application of Support Vector Machines (SVM)?
-
A.
Image classification
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B.
Data encryption
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C.
Web development
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D.
Database management
Solution
SVMs are widely used for image classification tasks due to their ability to handle high-dimensional data.
Correct Answer:
A
— Image classification
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Q. What is the primary goal of SVM in classification tasks?
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A.
Minimize the number of support vectors
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B.
Maximize the margin between classes
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C.
Minimize the classification error
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D.
Maximize the number of features
Solution
The main goal of SVM is to maximize the margin between the closest points of different classes.
Correct Answer:
B
— Maximize the margin between classes
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Q. What type of learning does SVM primarily utilize?
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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 used for classification and regression tasks.
Correct Answer:
A
— Supervised learning
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Q. Which kernel function is commonly used in SVM for non-linear classification?
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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.
Sigmoid kernel
Solution
The Radial Basis Function (RBF) kernel is popular for handling non-linear classification problems.
Correct Answer:
C
— Radial basis function (RBF) kernel
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Q. Which kernel is commonly used in SVM for non-linear data?
-
A.
Linear kernel
-
B.
Polynomial kernel
-
C.
Radial Basis Function (RBF) kernel
-
D.
Sigmoid kernel
Solution
The Radial Basis Function (RBF) kernel is commonly used in SVM to handle non-linear data effectively.
Correct Answer:
C
— Radial Basis Function (RBF) kernel
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Q. Which of the following fields has seen significant use of SVM?
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A.
Healthcare for disease classification
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B.
Manufacturing for process optimization
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C.
Finance for risk assessment
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D.
All of the above
Solution
SVM has applications across various fields, including healthcare, manufacturing, and finance.
Correct Answer:
D
— All of the above
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Q. Which of the following is a key advantage of using SVMs?
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A.
They require large amounts of data
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B.
They can handle non-linear boundaries
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C.
They are only suitable for binary classification
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D.
They are less interpretable than decision trees
Solution
SVMs can effectively handle non-linear boundaries through the use of kernel functions.
Correct Answer:
B
— They can handle non-linear boundaries
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Q. Which of the following is a key feature of SVMs?
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A.
They can only handle linear data
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B.
They use kernel functions to handle non-linear data
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C.
They require a large amount of labeled data
-
D.
They are not suitable for multi-class classification
Solution
SVMs utilize kernel functions to transform data into higher dimensions, allowing them to handle non-linear relationships.
Correct Answer:
B
— They use kernel functions to handle non-linear data
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Q. Which of the following is NOT a typical application of SVM?
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A.
Face detection
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B.
Spam detection
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C.
Stock price prediction
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D.
Handwriting recognition
Solution
While SVM can be used for regression, it is not typically the first choice for stock price prediction.
Correct Answer:
C
— Stock price prediction
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