[source] # Completeness 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. $$ {\displaystyle Completeness = 1-\frac{H(K, C)}{H(K)}} $$ !!! note 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 ## Parameters This metric does not have any parameters. ## Example ```php use Rubix\ML\CrossValidation\Metrics\Completeness; $metric = new Completeness(); ```