Quiz połączony z przewodnikiem · Średni Poziom
Quiz dotyczący grupowania hierarchicznego
Przeczytaj wysokości dendrogramów, rozróżnij reguły powiązań i zrozum, w jaki sposób wybory skalowania i cięcia wpływają na wyniki grupowania hierarchicznego.
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What sequence does agglomerative hierarchical clustering build?
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Odpowiedzi i wyjaśnienia pojawiają się w miarę rozwiązywania quizu.
- What sequence does agglomerative hierarchical clustering build?
- Repeated cluster merges from individual observations toward larger groups
- Repeatedly split one cluster into smaller groups from the top down
- Assign each point to a fixed centroid and update those centroids
- Join each point to its nearest neighbor once without building nested groups
- Which rule defines single linkage between two clusters?
- The maximum pairwise distance across clusters
- The minimum pairwise distance across clusters
- The average of all pairwise distances
- The increase in within-cluster sum of squares
- Which linkage uses the farthest cross-cluster pair?
- Average linkage
- Single linkage
- Complete linkage
- Ward linkage
- What does a dendrogram merge height encode?
- The p-value for a test that the two groups differ
- The number of observations contained in the merged branch
- The proportion of observations assigned to the left child
- The linkage distance or criterion value at that merge
- Why can a large jump in dendrogram height be useful?
- It proves the exact number of natural populations
- It may suggest candidate cuts that keep separated branches apart
- It means every leaf is an outlier
- It identifies the optimal feature scaling
- Why may single linkage create a chaining pattern?
- It requires all points to be equally distant
- It uses the average distance between every pair
- A sequence of near neighbors can connect otherwise elongated groups
- It minimizes centroid variance
- What does Ward linkage seek to minimize when merging?
- The entropy of labels
- The largest single pairwise distance only
- The number of leaves in the tree
- The increase in within-cluster sum of squares
- Why standardize features before Euclidean hierarchical clustering when units vary greatly?
- Otherwise a large-scale feature can dominate distances
- Scaling changes the dendrogram into a supervised model
- It forces average linkage to equal Ward linkage
- It guarantees that the chosen cut is stable