Support Vector Machines (SVMs) models were built to predict categorical and continuous outcomes and are especially good when you have many predictors. They were developed for difficult predicting situations where linear models were unable to separate the categories of the outcome field. They too work like black boxes, hiding their complex work in predicting results. Let's get an insight into how SVMs work.
Support Vector Machines
Working with Support Vector Machines
Suppose, for example, there is a kind of data that cannot be separated using a single line as shown in this diagram:
Consider these shapes to be different types of data. As you can see, we won't be able to separate a cluster of data by just drawing a...
