sklearn.metrics.homogeneity_score(labels_true, labels_pred)
[source]
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: |
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Returns: |
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See also
[1] | Andrew Rosenberg and Julia Hirschberg, 2007. V-Measure: A conditional entropy-based external cluster evaluation measure |
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...
sklearn.metrics.homogeneity_score
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Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_score.html