Glossary · Phase 3 · Machine learning
Elbow method
Picking the number of clusters where adding more stops making the clusters much tighter: the bend in the curve.
Try it now
Press Next step to alternate the two moves from this section. New start shows that the first guess matters.
Learn it properly
Clustering · Group similar items without labels (k-means). About 35 minutes.
Other terms from the same lesson
- clustering: Grouping similar examples together without being told the groups in advance.
- inertia: How tight a k-means clustering is: the total squared distance from each point to its cluster's center. Lower is tighter.
- silhouette score: How well each point sits in its own cluster rather than the next one: near 1 is clear-cut, near 0 is on a border.
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