特征与多项式回归 Features and Polynomial Regression

We can improve our features and the form of our hypothesis function in a couple different ways.

We can combine multiple features into one. For example, we can combine 2.

Polynomial Regression

Our hypothesis function need not be linear (a straight line) if that does not fit the data well.

We can change the behavior or curve of our hypothesis function by making it a quadratic, cubic or square root function (or any other form).

For example, if our hypothesis function is 1

In the cubic version, we have created new features 1.

To make it a square root function, we could do: 1−−√

One important thing to keep in mind is, if you choose your features this way then feature scaling becomes very important.

eg. if 1 becomes 1 - 1000000000