What is a support vector machine (SVM) in machine learning?
a) A supervised learning model used for classification and regression tasks
b) An unsupervised learning model used for clustering
c) A model used to reduce the dimensionality of data
d) A model used for time series forecasting
Answer:
a) A supervised learning model used for classification and regression tasks
Explanation:
Support Vector Machines (SVMs) are supervised learning models used for classification and regression tasks. The main idea of SVM is to find a hyperplane that best separates the data points into different classes in a high-dimensional space.
SVM works by maximizing the margin between the hyperplane and the nearest data points from each class, known as support vectors. This makes it effective for classification tasks with clear boundaries between classes.
SVM is also effective in high-dimensional spaces and can be extended to handle non-linear classification by using the kernel trick, which allows it to perform well on complex datasets.