# sklearn.metrics.homogeneity_score¶

sklearn.metrics.homogeneity_score(labels_true, labels_pred)[source]

Homogeneity metric of a cluster labeling given a ground truth.

A clustering result satisfies homogeneity if all of its clusters contain only data points which are members of a single class.

This metric is independent of the absolute values of the labels: a permutation of the class or cluster label values won’t change the score value in any way.

This metric is not symmetric: switching label_true with label_pred will return the completeness_score which will be different in general.

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.

Returns:
homogeneityfloat

Score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling.

completeness_score

Completeness metric of cluster labeling.

v_measure_score

V-Measure (NMI with arithmetic mean option).

References

Examples

Perfect labelings are homogeneous:

>>> from sklearn.metrics.cluster import homogeneity_score
>>> homogeneity_score([0, 0, 1, 1], [1, 1, 0, 0])
1.0


Non-perfect labelings that further split classes into more clusters can be perfectly homogeneous:

>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 1, 2]))
1.000000
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3]))
1.000000


Clusters that include samples from different classes do not make for an homogeneous labeling:

>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 0, 1]))
0.0...
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 0, 0]))
0.0...


## Examples using sklearn.metrics.homogeneity_score¶

A demo of K-Means clustering on the handwritten digits data

A demo of K-Means clustering on the handwritten digits data

Demo of DBSCAN clustering algorithm

Demo of DBSCAN clustering algorithm

Demo of affinity propagation clustering algorithm

Demo of affinity propagation clustering algorithm

Clustering text documents using k-means

Clustering text documents using k-means