sklearn.metrics.homogeneity_completeness_v_measure¶
- 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.
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
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