Maximum Margin Hyperplane

The Maximum Margin Hyperplane is a method used in machine learning to classify data points into different groups. It finds the best dividing line (or boundary) that separates these groups with the widest possible gap, called the margin. By maximizing this margin, the model improves its ability to correctly classify new, unseen data, because the boundary is chosen to be as far away as possible from all examples in each group. This approach helps create a robust and reliable classifier that balances accuracy and generalization.