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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.

Completeness=1H(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

Output Range: 0 to 1

Parameters#

This metric does not have any parameters.

Example#

use Rubix\ML\CrossValidation\Metrics\Completeness;

$metric = new Completeness();

Last update: 2021-01-26