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

sklearn.metrics.homogeneity_score(labels_true, labels_pred)

  Homogeneity metric of a cluster labeling given a ground truth.

  A clustering result satisfies homogeneity if all of its clusters contain only data points which are members of a single class.

  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 `completeness_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:
    homogeneity float

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

  See also

  `completeness_score`

    Completeness 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 homogeneous:

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

  Non-perfect labelings that further split classes into more clusters can be perfectly homogeneous:

      >>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 1, 2]))
      1.000000
      >>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3]))
      1.000000

  Clusters that include samples from different classes do not make for an homogeneous labeling:

      >>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 0, 1]))
      0.0...
      >>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 0, 0]))
      0.0...

## Examples using `sklearn.metrics.homogeneity_score`

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