ML 3.7Lesson outlineLesson 7 of 7 in Module 3
Collinearity
See why correlated features make individual coefficients unstable while the predictions stay good, and measure how unstable they are.
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Lesson outline: Collinearity
In this lesson8 parts
- 01Four engine columns
- 02Columns that move together
- 03Fitting again on resampled cars
- 04The variance inflation factor
- 05When a column is an exact copy
- 06What to do about it
- 07Code
- 08Recap, your challenge, and what comes next
Builds on
This lesson stands on its own. These go deeper into what it uses.
- ML 3.4: Multiple Linear Regression
- Stats 1.6; Maths 3.3 (²) named for the second pass: Correlation, Confounding and Causation
- not depended on (3.7 is on the core path; checked 2026-10-14)
Key terms: Collinearity
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.
- collinearity
- Features that are close to linear functions of each other.
- multicollinearity
- Collinearity among more than two features.
- bootstrap resample
- A sample of the same size drawn from the data with replacement.
- variance inflation factor
- How many times larger a coefficient's variance is because the feature can be predicted from the other features:
1 / (1 − R²). - perfect collinearity
- A feature that is an exact linear function of other features (singular matrices: Maths 3.3, second pass).
- dummy-variable trap
- One 0/1 column for every category, beside an intercept, which makes the columns perfectly collinear.
Quiz: Collinearity
Your first pick on each question is the one that counts. Getting one wrong here is how the lesson sticks.