ML 4.7Lesson outlineLesson 7 of 9 in Module 4
Polynomial Regression: Choosing Model Capacity
Fit curves with a linear model through basis functions, and choose the degree by validation.
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Lesson outline: Polynomial Regression: Choosing Model Capacity
In this lesson12 parts
- 01Fuel economy against horsepower bends
- 02The training cars with horsepower
- 03A straight line
- 04Powers as columns
- 05Weights, and a degree
- 06The same squared error
- 07Least squares on the new columns
- 08Predicting
- 09Choosing the degree
- 10Too many degrees
- 11Code
- 12Recap, your challenge, and what comes next
Builds on
This lesson stands on its own. These go deeper into what it uses.
Key terms: Polynomial Regression: Choosing Model Capacity
The words this lesson introduces, each in one line. The module’s glossary collects them all, and the AI glossary has every term across the courses.
- polynomial regression
- Linear regression on powers of a feature.
- polynomial features
- The powers added as columns.
- basis function
- A transformed feature used as a column.
- degree
- The highest power in a polynomial model.
- capacity (reminder, ML 4.3)
- Here, the degree.
- one-standard-error rule (reminder, ML 4.5)
- Here, the smallest degree within one standard error of the best.
- interaction term
- The product of two features.
- spline
- A curve built from joined low-degree pieces.
Quiz: Polynomial Regression: Choosing Model Capacity
Your first pick on each question is the one that counts. Getting one wrong here is how the lesson sticks.