Apply K-means and hierarchical clustering to transform data.
Free SOA Exam PA (Predictive Analytics) lesson in Data Transformations and Unsupervised Learning Techniques. 12 min read, ~1,852 words.
Clustering is unsupervised: it groups observations by similarity with no target variable. K-means needs you to fix the number of clusters K in advance; hierarchical does not. Standardize features first, or the largest-scale variable dominates the distance. Use an elbow plot (within-cluster sum of squares vs K) to choose K...
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What this lesson covers
- Content
- Example 1
- Example 2
- Common Mistakes
- Check Your Understanding
- Exam Shortcuts
Learning objectives
- 3c
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