Support Vector Machines (SVMs) are really good at solving tricky problems where we need to sort things into different groups. They do this by finding the best boundaries, known as hyperplanes, in complicated spaces.

Key Features:

• Kernel Trick: This cool technique helps SVMs work in new spaces created from the original data. It lets them manage relationships that aren’t straight lines, making it easier to sort the data.
• Margin Maximization: SVMs make sure there is a big gap between the different groups. This gap helps prevent mistakes when classifying new data.

Performance Statistics:

• In 2018, SVMs got more than 95% accuracy on various important tests.
• They are especially accurate for text classification, reaching up to 98% accuracy!

In short, SVMs are better at handling complicated data compared to older methods like linear regression and decision trees.