Support Vector Machines Overview - Competitive Exam Prep

Support Vector Machines Overview - Competitive Exam Level

Q. In which scenario would you prefer using SVM over other algorithms?

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

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

Solution

The parameter 'C' controls the trade-off between maximizing the margin and minimizing the classification error.

Correct Answer: A — The complexity of the model

Q. Which of the following is a characteristic of SVM?

A. It can only be used for binary classification
B. It is sensitive to outliers
C. It can handle multi-class classification using one-vs-one or one-vs-all strategies
D. It requires a large amount of labeled data

Solution

SVM can handle multi-class classification problems using strategies like one-vs-one or one-vs-all.

Correct Answer: C — It can handle multi-class classification using one-vs-one or one-vs-all strategies

Q. Which of the following is a key advantage of using SVM?

Solution

SVM is particularly effective in high-dimensional spaces, making it suitable for various applications, including text classification.

Correct Answer: C — It is effective in high-dimensional spaces

Q. Which of the following is NOT a common application of SVM?

Solution

SVM is primarily used for classification tasks, while clustering is typically handled by different algorithms.

Correct Answer: D — Clustering of data

Q. Which of the following is NOT a type of SVM?

Solution

K-Means SVM is not a recognized type of SVM; the other options are valid types of Support Vector Machines.

Correct Answer: D — K-Means SVM

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