Support Vector Machines Overview for Competitive Exams

Support Vector Machines Overview - Case Studies

Q. In a case study, SVM was used to classify emails as spam or not spam. What type of learning is this an example of?

A. Unsupervised learning
B. Reinforcement learning
C. Supervised learning
D. Semi-supervised learning

Solution

Classifying emails as spam or not spam is an example of supervised learning, where the model is trained on labeled data.

Correct Answer: C — Supervised learning

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Q. In a real-world application, SVM can be used for which of the following?

A. Image recognition
B. Time series forecasting
C. Clustering customer segments
D. Generating text

Solution

SVM is widely used in image recognition tasks, where it can classify images based on their features.

Correct Answer: A — Image recognition

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Q. In which scenario would you prefer using SVM over decision trees?

A. When interpretability is crucial
B. When the dataset is very large
C. When the data is high-dimensional and sparse
D. When the data is categorical

Solution

SVM is preferred in high-dimensional and sparse datasets, as it can effectively find the optimal hyperplane in such spaces.

Correct Answer: C — When the data is high-dimensional and sparse

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Q. What does the 'C' parameter in SVM control?

A. The number of support vectors
B. The trade-off between maximizing the margin and minimizing classification error
C. The complexity of the kernel function
D. The learning rate of the model

Solution

The 'C' parameter in SVM controls the trade-off between maximizing the margin and minimizing the classification error on the training data.

Correct Answer: B — The trade-off between maximizing the margin and minimizing classification error

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Q. What is a common application of SVM in the real world?

A. Image recognition
B. Time series forecasting
C. Clustering customer segments
D. Reinforcement learning

Solution

SVM is commonly applied in image recognition tasks, where it can classify images based on their features.

Correct Answer: A — Image recognition

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Q. What is a common challenge when using SVM for large datasets?

A. High interpretability
B. Scalability and computational cost
C. Low accuracy
D. Limited feature selection

Solution

SVM can be computationally intensive and may struggle with scalability when dealing with large datasets due to the complexity of the optimization problem.

Correct Answer: B — Scalability and computational cost

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Q. What is the effect of using a linear kernel in SVM?

A. It allows for non-linear decision boundaries
B. It simplifies the model and reduces computation
C. It increases the risk of overfitting
D. It can only classify linearly separable data

Solution

Using a linear kernel in SVM means that the model can only create linear decision boundaries, making it suitable for linearly separable data.

Correct Answer: D — It can only classify linearly separable data

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Q. What is the primary purpose of Support Vector Machines (SVM)?

A. To perform clustering on unlabeled data
B. To classify data into distinct categories
C. To reduce dimensionality of data
D. To generate synthetic data

Solution

Support Vector Machines are primarily used for classification tasks, where they aim to find the optimal hyperplane that separates different classes in the feature space.

Correct Answer: B — To classify data into distinct categories

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Q. What role does the 'C' parameter play in SVM?

A. It controls the number of support vectors
B. It determines the kernel type
C. It balances the trade-off between maximizing the margin and minimizing classification error
D. It sets the learning rate

Solution

The 'C' parameter in SVM controls the trade-off between maximizing the margin and minimizing classification errors, influencing the model's complexity.

Correct Answer: C — It balances the trade-off between maximizing the margin and minimizing classification error

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Q. Which evaluation metric is most appropriate for assessing the performance of an SVM model in a binary classification task?

A. Mean Squared Error
B. Accuracy
C. Silhouette Score
D. Adjusted R-squared

Solution

Accuracy is a common evaluation metric for binary classification tasks, measuring the proportion of correctly classified instances.

Correct Answer: B — Accuracy

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Support Vector Machines Overview - Case Studies

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