What is a support vector machine primarily used for?

A support vector machine (SVM) is primarily utilized for classification and regression analysis due to its effectiveness in finding the optimal hyperplane that separates data into different classes. In classification tasks, SVMs work by mapping input features into high-dimensional space and identifying the hyperplane that maximally separates the classes. This approach enables SVMs to correctly categorize new data points based on their proximity to the hyperplane.

In regression analysis, SVMs can be adapted to predict continuous outcomes by determining a function that approximates the relationship between the input features and the target variable. This flexibility makes SVMs a powerful tool in various domains such as image recognition, text classification, and bioinformatics, where precise classification or predictive modeling is crucial.

Other options like data cleaning and storage, data visualization, and gathering unstructured data are not primary functions of SVMs. While these tasks are important in data science, they fall outside the direct capabilities of SVMs, which are specifically designed for analyzing relationships in labeled datasets.