Polynomial and Support Vector Regression
When performing polynomial regression, the relationship between x and y, or using their other names, features and labels, is not a linear equation, but a polynomial equation. This means that instead of the y = a*x+b equation, we can have multiple coefficients and multiple powers of x in the equation.
To make matters even more complicated, we can perform polynomial regression using multiple variables, where each feature may have coefficients multiplying different powers of the feature.
Our task is to find a curve that best fits our dataset. Once polynomial regression is extended to multiple variables, we will learn the Support Vector Machines model to perform polynomial regression.
Polynomial Regression with One Variable
As a recap, we have performed two types of regression so far:
Simple linear regression: y = a*x + b
Multiple linear regression: y = b + a1 * x1 + a2 * x2 + … + an * xn
We will now learn how to do polynomial linear regression with one...
