The correct answer is: A. classification.
Support vector machines (SVMs) are a type of supervised machine learning algorithm that can be used for both classification and regression tasks. However, they are most commonly used for classification tasks.
SVMs work by finding a hyperplane in a high-dimensional space that separates the data points into two classes. The hyperplane is chosen such that it maximizes the margin between the two classes. This means that the data points on the boundary between the two classes are as far apart as possible.
SVMs are a powerful machine learning algorithm that can be used to solve a variety of problems. They are particularly well-suited for problems where the data is linearly separable.
Here is a brief explanation of each option:
- Classification: Classification is the task of assigning labels to data points. For example, in the problem of spam filtering, the goal is to assign a label of “spam” or “not spam” to each email.
- Clustering: Clustering is the task of grouping data points together based on their similarity. For example, in the problem of image segmentation, the goal is to group pixels together based on their color.
- Regression: Regression is the task of predicting a continuous value from a set of data points. For example, in the problem of predicting house prices, the goal is to predict the price of a house given its location, size, and other features.