Why PCA has value. When predictors are highly correlated, they carry redundant information and destabilize regression coefficients. PCA rotates the data onto new axes, the principal components, that are uncorrelated with each other. The first few often retain most of the variance, so you can drop the rest. That reduces...
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What this lesson covers
- Content
- Example 1
- Example 2
- Common Mistakes
- Check Your Understanding
- Bottom Line
- Exam Shortcuts
Learning objectives
- 3b
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