Principal Components Analysis
Free SOA Exam SRM (Statistics for Risk Modeling) lesson in Unsupervised Learning Techniques. 18 min read, ~2,737 words.
Principal components are linear combinations of the original predictors that capture maximum variance, subject to being uncorrelated with earlier components. The first PC is the direction in feature space along which the data vary most; the second PC is the highest-variance direction orthogonal to the first. Loadings are the coefficients...
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What this lesson covers
- Content
- Example 1
- Example 2
- Example 3
- Example 4
- Example 5
- Common Mistakes
- Key Takeaways
- Exam Shortcuts
Learning objectives
- 5a
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