Course Content
Linear Regression with Python
In the previous chapter, we used a function from NumPy to calculate the parameters.
Now we will use the class object instead of the function to represent the linear regression. This approach takes more lines of code to find the parameters, but it stores a lot of helpful information inside the object and makes the prediction more straightforward.
Building a Linear Regression model
In statsmodels, the OLS
class can be used to create a linear regression model.

We first need to initialize an OLS
class object using
sm.OLS(y, X_tilde)
.
Then train it using the fit()
method.
Which is equivalent to:
Note
The constructor of the
OLS
class expects a specific arrayX_tilde
as an input, which we saw in the Normal Equation. So you need to convert yourX
array toX_tilde
. This is achievable using thesm.add_constant()
function.
Finding parameters
When the model is trained, you can easily access the parameters using the params
attribute.
Making the predictions
New instances can easily be predicted using predict()
method, but you need to preprocess the input for them too:
Getting the summary
As you probably noticed, using the OLS
class is not as easy as the polyfit()
function. But using OLS
has its benefits. While training, it calculates a lot of statistical information. You can access the information using the summary()
method.
That's a lot of statistics. We will discuss the table's most important parts in later sections.
Choose the INCORRECT statement.
Select the correct answer
Section 1.
Chapter 4