sklearn.metrics.silhouette_score(X, labels, metric=’euclidean’, sample_size=None, random_state=None, **kwds)
[source]
Compute the mean Silhouette Coefficient of all samples.
The Silhouette Coefficient is calculated using the mean intracluster distance (a
) and the mean nearestcluster distance (b
) for each sample. The Silhouette Coefficient for a sample is (b  a) / max(a,
b)
. To clarify, b
is the distance between a sample and the nearest cluster that the sample is not a part of. Note that Silhouette Coefficient is only defined if number of labels is 2 <= n_labels <= n_samples  1.
This function returns the mean Silhouette Coefficient over all samples. To obtain the values for each sample, use silhouette_samples
.
The best value is 1 and the worst value is 1. Values near 0 indicate overlapping clusters. Negative values generally indicate that a sample has been assigned to the wrong cluster, as a different cluster is more similar.
Read more in the User Guide.
Parameters: 


Returns: 

[1]  Peter J. Rousseeuw (1987). “Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis”. Computational and Applied Mathematics 20: 5365. 
[2]  Wikipedia entry on the Silhouette Coefficient 
sklearn.metrics.silhouette_score
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Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html