sklearn.metrics.homogeneity_completeness_v_measure — scikit-learn 0.20.4 documentation (original) (raw)
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 andlabel_pred will give the same score. This does not hold for homogeneity and completeness. V-Measure is identical tonormalized_mutual_info_score with the arithmetic averaging method.
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 |