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 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_trueint array, shape = [n_samples]
ground truth class labels to be used as a reference
- labels_predarray-like of shape (n_samples,)
cluster labels to evaluate
- betafloat, default=1.0
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