Why penalize at all. Ordinary least squares minimizes squared error and nothing else. With many predictors, many-level categoricals, or correlated columns, ordinary least squares (OLS) chases noise and coefficient estimates swing wildly. Regularization adds a penalty term that grows as coefficients grow. The model must now balance fitting the data...
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What this lesson covers
- Content
- Example 1
- Example 2
- Common Mistakes
- Check Your Understanding
- Bottom Line
- Exam Shortcuts
Learning objectives
- 4d
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