sklearn.metrics
.homogeneity_completeness_v_measure¶

sklearn.metrics.
homogeneity_completeness_v_measure
(labels_true, labels_pred, *, beta=1.0)[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
andlabel_pred
will give the same score. This does not hold for homogeneity and completeness. VMeasure is identical tonormalized_mutual_info_score
with the arithmetic averaging method.Read more in the User Guide.
 Parameters
 labels_trueint array, shape = [n_samples]
ground truth class labels to be used as a reference
 labels_predarraylike of shape (n_samples,)
cluster labels to evaluate
 betafloat
Ratio of weight attributed to
homogeneity
vscompleteness
. Ifbeta
is greater than 1,completeness
is weighted more strongly in the calculation. Ifbeta
is less than 1,homogeneity
is weighted more strongly.
 Returns
 homogeneityfloat
score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling
 completenessfloat
score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling
 v_measurefloat
harmonic mean of the first two