sklearn.metrics.homogeneity_completeness_v_measure(labels_true, labels_pred)
[source]
Compute the homogeneity and completeness and V-Measure 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.
V-Measure is furthermore symmetric: swapping labels_true
and label_pred
will give the same score. This does not hold for homogeneity and completeness.
Read more in the User Guide.
Parameters: |
labels_true : int array, shape = [n_samples] ground truth class labels to be used as a reference labels_pred : array, shape = [n_samples] cluster labels to evaluate |
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Returns: |
homogeneity: float : score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling completeness: float : score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling v_measure: float : harmonic mean of the first two |
See also
© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.html