Hyperplanes and You: Support Vector Machines
supraspatial decisionmaking
A core data science task is classification: grouping data points into various groups based on certain shared qualities.
In a sense, it’s an exercise as old as life itself: as soon as the first protozoan developed sensory organs, it (accidentally) started to act differently based on various sensory stimulus.
On a higher biological level, it’s a monkey looking at an object hanging from a branch and deciding “food” or “not food”.
On a machine level, it’s your ML model combing through credit transactions and deciding “fraud” or “not fraud”.
You’ve probably heard of clustering as a technique for classification; it’s easy enough to visualize on a two-dimensional graph, or even with a Z axis added in.
It’s intuitive, since we move about in three, maybe four dimensions.
But your data may be a little more complex than that (as far as axes are concerned), and the moment you have 4 columns in your table, you’re in high-dimensional space.
