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A ground-truth clustering metric that measures the ratio of samples in a class that are also members of the same cluster. A cluster is said to be complete when all the samples in a class are contained in a cluster.

\[ {\displaystyle Completeness = 1-\frac{H(K, C)}{H(K)}} \]


Since this metric monotonically improves as the number of target clusters decreases, it should not be used as a metric to guide hyper-parameter tuning.

Estimator Compatibility: Clusterer

Score Range: 0 to 1


This metric does not have any parameters.


use Rubix\ML\CrossValidation\Metrics\Completeness;

$metric = new Completeness();