What are Support Vector Machines?
Support Vector Machines (SVMs) is a type of algorithm that's like the cool, edgy aunt of machine learning.
SVMs are all about finding the best way to separate two groups of data in a high-dimensional space. They're like the bouncers at a trendy nightclub: they keep the party going, but only let in the cool kids.
But don't let their tough exterior fool you - SVMs are actually pretty softies at heart. They just want to make sure everyone gets along and doesn't get too rowdy.
How do SVMs Work?
SVMs work by creating a hyperplane that maximizes the margin between two classes. It's like they're trying to throw the hottest party in town, and they want to make sure the line between the cool kids and the not-so-cool kids is as clear as day.
But wait, there's more! SVMs can also be used for classification and regression tasks. They're like the ultimate multi-tasker - they can be a bouncer, a DJ, and a bartender all at once.
Want to learn more about the world of Supervised Learning? Check out our subpage on Linear Regression: The Party's Favorite Math Whiz.
Or if you're feeling adventurous, try our subpage on Decision Trees: The Party's Favorite Karaoke DJ.