Glossary · Phase 3 · Machine learning
Inertia
How tight a k-means clustering is: the total squared distance from each point to its cluster's center. Lower is tighter.
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.
- 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.