Machine Learning Quick Reference

Hyperplanes 

Many of you will have guessed it right. We use hyperplanes when it comes to more than 3D. We will define it using a bit of mathematics.

A linear equation looks like this: y = ax + b has got two variables, x and y, and a y-intercept, which is b. If we rename y as x₂ and x as x₁, the equation comes out as x₂=ax₁ + b which implies ax₁ - x₂ + b=0. If we define 2D vectors as x= (x₁,x₂) and w=(a,-1) and if we make use of the dot product, then the equation becomes w.x + b = 0. 

Remember, x.y = x₁y₁ + x₂y₂.

So, a hyperplane is a set of points that satisfies the preceding equation. But how do we classify with the help of hyperplane?

We define a hypothesis function h:

h(x_i) = +1 if w.x_i + b ≥ 0

-1 if w.x_i + b < 0

This could be equivalent ...