What algorithm classifies data by finding the optimal hyperplane that maximizes the margin between classes?

The classification of data by finding an optimal hyperplane that maximizes the margin between different classes is a fundamental characteristic of Support Vector Machines (SVM). In SVM, the objective is to determine a hyperplane that separates the data points of different classes while being as far away from the nearest data points in each class as possible. This distance is known as the margin, and maximizing it helps improve the generalization capabilities of the classifier.

SVM is particularly effective for high-dimensional data and is well-suited for problems where the classes are not linearly separable, as it can utilize techniques like the kernel trick to transform data into higher dimensions where it becomes possible to find a separating hyperplane. The focus on the margin is critical because a larger margin reduces the likelihood of overfitting the training data.

In contrast, the other algorithms mentioned do not operate based on the concept of hyperplanes and margins in the same way. Decision Trees make classifications based on a series of rule-based splits rather than the geometry of the data space. Random Forest is an ensemble method built from multiple decision trees and similarly does not rely on hyperplanes. Naive Bayes uses probabilistic approaches to make classifications based on feature independence, distinctly different from the geometric approach of S