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Linear Regression with Python
course content

Kursusindhold

Linear Regression with Python

Linear Regression with Python

1. Simple Linear Regression
What is Linear RegressionFinding the ParametersBuilding Linear Regression Using NumPyBuilding Linear Regression Using StatsmodelsPredict House Prices
2. Multiple Linear Regression
Linear Regression with Two FeaturesLinear Regression with n FeaturesBuilding Multiple Linear RegressionChoosing the FeaturesPredict Prices Using Two Features
3. Polynomial Regression
Quadratic RegressionPolynomial RegressionBuilding Polynomial RegressionInterpolation vs ExtrapolationEvaluate the Model
4. Choosing The Best Model
MetricsOverfittingR-squaredPredict Prices Using Polynomial Regression

book
Polynomial Regression

In the previous chapter, we explored quadratic regression, which has the graph of a parabola. In the same way, we could add the x³ to the equation to get the Cubic Regression that has a more complex graph. We could also add x⁴ and so on.

A degree of a Polynomial Regression

In general, it is called the polynomial equation and is the equation of Polynomial Regression. The highest power of x defines a degree of a Polynomial Regression in the equation. Here is an example

n-degree Polynomial Regression

Considering n to be a whole number greater than two, we can write down the equation of an n-degree Polynomial Regression.

Normal Equation

And as always, parameters are found using the Normal Equation:

Polynomial Regression with multiple features

To create even more complex shapes, you can use the Polynomial Regression with more than one feature. But even with two features, 2-degree Polynomial Regression has quite a long equation.

Most of the time, you won't need that complex model. Simpler models (like Multiple Linear Regression) usually describe the data well enough, and they are much easier to interpret, visualize and less computationally expensive.

question mark

Choose the INCORRECT statement.

Select the correct answer

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Sektion 3. Kapitel 2

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course content

Kursusindhold

Linear Regression with Python

Linear Regression with Python

1. Simple Linear Regression
What is Linear RegressionFinding the ParametersBuilding Linear Regression Using NumPyBuilding Linear Regression Using StatsmodelsPredict House Prices
2. Multiple Linear Regression
Linear Regression with Two FeaturesLinear Regression with n FeaturesBuilding Multiple Linear RegressionChoosing the FeaturesPredict Prices Using Two Features
3. Polynomial Regression
Quadratic RegressionPolynomial RegressionBuilding Polynomial RegressionInterpolation vs ExtrapolationEvaluate the Model
4. Choosing The Best Model
MetricsOverfittingR-squaredPredict Prices Using Polynomial Regression

book
Polynomial Regression

In the previous chapter, we explored quadratic regression, which has the graph of a parabola. In the same way, we could add the x³ to the equation to get the Cubic Regression that has a more complex graph. We could also add x⁴ and so on.

A degree of a Polynomial Regression

In general, it is called the polynomial equation and is the equation of Polynomial Regression. The highest power of x defines a degree of a Polynomial Regression in the equation. Here is an example

n-degree Polynomial Regression

Considering n to be a whole number greater than two, we can write down the equation of an n-degree Polynomial Regression.

Normal Equation

And as always, parameters are found using the Normal Equation:

Polynomial Regression with multiple features

To create even more complex shapes, you can use the Polynomial Regression with more than one feature. But even with two features, 2-degree Polynomial Regression has quite a long equation.

Most of the time, you won't need that complex model. Simpler models (like Multiple Linear Regression) usually describe the data well enough, and they are much easier to interpret, visualize and less computationally expensive.

question mark

Choose the INCORRECT statement.

Select the correct answer

Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 2
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