Linear Classifiers
Explore linear classifiers, their principles, and their training process.
We'll cover the following...
What is a linear classifier?
Suppose we want to build a machine learning model to classify the following points into two categories based on their color. It is very easy to see that we can find a single point that separates them perfectly. The goal of our model is to find this point.
The easiest way to do that is to build a linear classifier. Our classifier has the form . The purpose of will be to find the parameters and , so that any corresponding scalar point (1D) can be distinguished perfectly. If , the point belongs to the blue category. Otherwise, it belongs to the red. Sounds easy?
Let’s extend this idea to 2D data points!
Each point will now be represented as .
For the 2D case, we need to find a line (instead of a point) that separates our 2D points, so our classifier will be ...