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scikit-learn 1.3.2
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# `sklearn.metrics`.completeness_score

sklearn.metrics.completeness_score(labels_true, labels_pred)

  Compute completeness metric of a cluster labeling given a ground truth.

  A clustering result satisfies completeness if all the data points that are members of a given class are elements of the same cluster.

  This metric is independent of the absolute values of the labels: a permutation of the class or cluster label values won’t change the score value in any way.

  This metric is not symmetric: switching `label_true` with `label_pred` will return the `homogeneity_score` which will be different in general.

  Read more in the User Guide.

  Parameters:
    labels_true array-like of shape (n_samples,)

      Ground truth class labels to be used as a reference.

    labels_pred array-like of shape (n_samples,)

      Cluster labels to evaluate.

  Returns:
    completeness float

      Score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling.

  See also

  `homogeneity_score`

    Homogeneity metric of cluster labeling.

  `v_measure_score`

    V-Measure (NMI with arithmetic mean option).

  References

  [1]

  Andrew Rosenberg and Julia Hirschberg, 2007. V-Measure: A conditional entropy-based external cluster evaluation measure

  Examples

  Perfect labelings are complete:

      >>> from sklearn.metrics.cluster import completeness_score
      >>> completeness_score([0, 0, 1, 1], [1, 1, 0, 0])
      1.0

  Non-perfect labelings that assign all classes members to the same clusters are still complete:

      >>> print(completeness_score([0, 0, 1, 1], [0, 0, 0, 0]))
      1.0
      >>> print(completeness_score([0, 1, 2, 3], [0, 0, 1, 1]))
      0.999...

  If classes members are split across different clusters, the assignment cannot be complete:

      >>> print(completeness_score([0, 0, 1, 1], [0, 1, 0, 1]))
      0.0
      >>> print(completeness_score([0, 0, 0, 0], [0, 1, 2, 3]))
      0.0

## Examples using `sklearn.metrics.completeness_score`

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.23

A demo of K-Means clustering on the handwritten digits data

A demo of K-Means clustering on the handwritten digits data

Demo of DBSCAN clustering algorithm

Demo of DBSCAN clustering algorithm

Demo of affinity propagation clustering algorithm

Demo of affinity propagation clustering algorithm

Clustering text documents using k-means

Clustering text documents using k-means