sklearn.metrics.homogeneity_completeness_v_measure(labels_true, labels_pred)
[source]
Compute the homogeneity and completeness and VMeasure scores at once.
Those metrics are based on normalized conditional entropy measures of the clustering labeling to evaluate given the knowledge of a Ground Truth class labels of the same samples.
A clustering result satisfies homogeneity if all of its clusters contain only data points which are members of a single class.
A clustering result satisfies completeness if all the data points that are members of a given class are elements of the same cluster.
Both scores have positive values between 0.0 and 1.0, larger values being desirable.
Those 3 metrics are independent of the absolute values of the labels: a permutation of the class or cluster label values won’t change the score values in any way.
VMeasure is furthermore symmetric: swapping labels_true
and label_pred
will give the same score. This does not hold for homogeneity and completeness. VMeasure is identical to normalized_mutual_info_score
with the arithmetic averaging method.
Read more in the User Guide.
Parameters: 


Returns: 

See also
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.html