- 1. A support vector machine (SVM) is a supervised machine learning algorithm that is commonly used for classification and regression tasks. The goal of SVM is to find the hyperplane that best separates the data points into different classes, with a clear margin between the classes. SVM works by mapping the input data into a high-dimensional feature space and finding the optimal hyperplane that maximizes the margin between the classes. This optimal hyperplane is found by solving an optimization problem that aims to minimize the classification error and maximize the margin. SVM is known for its ability to handle high-dimensional data and complex classification tasks. It is also effective in dealing with non-linear data by using kernel functions to map the data into a higher-dimensional space. SVM is widely used in various applications such as text classification, image recognition, and bioinformatics due to its flexibility, accuracy, and robustness.
What is a Support Vector Machine (SVM) used for?
A) Image processing
B) Speech recognition
C) Classification and regression
D) Video editing
- 2. What is the kernel trick in SVM?
A) Simplifying the decision boundary
B) Adding noise to the data
C) Mapping data into higher-dimensional space
D) Removing outliers
- 3. Which kernel is commonly used in SVM for non-linear classification?
A) Polynomial kernel
B) RBF (Radial Basis Function)
C) Sigmoid kernel
D) Linear kernel
- 4. What is regularization parameter C in SVM?
A) Number of dimensions
B) Trade-off between margin and error
C) Kernel parameter
D) Number of support vectors
- 5. What is the loss function used in SVM?
A) Mean squared error
B) Cross-entropy loss
C) L2 regularization
D) Hinge loss
- 6. Which optimization algorithm is commonly used in SVM training?
A) Gradient Descent
B) Sequential Minimal Optimization (SMO)
C) Adam
D) Newton's Method
- 7. What is the kernel trick in SVM used for?
A) Removing noise in the data
B) Efficiently handling non-linear separable data
C) Preventing overfitting
D) Simplifying the model complexity
- 8. What is the role of the kernel function in SVM?
A) Calculating margin width
B) Selecting support vectors
C) Updating model weights
D) Mapping input data into a higher-dimensional space
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