Glossary · Phase 3 · Machine learning
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.
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.
- elbow method: Picking the number of clusters where adding more stops making the clusters much tighter: the bend in the curve.
- inertia: How tight a k-means clustering is: the total squared distance from each point to its cluster's center. Lower is tighter.
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