Select appropriate hyperparameters for regularized regression.
Free SOA Exam PA (Predictive Analytics) lesson in Generalized Linear Models. 15 min read, ~2,220 words.
Regularization adds a penalty on coefficient size to the loss, trading a little bias for a large drop in variance. Ridge uses an L2 penalty (sum of squared coefficients); it shrinks toward zero but never exactly to zero, so all predictors stay in. Lasso uses an L1 penalty (sum of...
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- Content
- Example 1
- Example 2
- Common Mistakes
- Check Your Understanding
- Exam Shortcuts
Learning objectives
- 4d
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