There's more…

We built a simple classifier using the following rule—the input point (a, b) belongs to class_0 if a is greater than or equal to b; otherwise, it belongs to class_1. If you inspect the points one by one, you will see that this is, in fact, true. That's it! You just built a linear classifier that can classify unknown data. It's a linear classifier because the separating line is a straight line. If it's a curve, then it becomes a nonlinear classifier.

This formation worked well, because there were a limited number of points, and we could visually inspect them. What if there were thousands of points? How would we generalize this process? Let's discuss that in the next recipe.