sklearn.metrics.silhouette_score(X, labels, metric=’euclidean’, sample_size=None, random_state=None, **kwds)
Compute the mean Silhouette Coefficient of all samples.
The Silhouette Coefficient is calculated using the mean intra-cluster distance (
a) and the mean nearest-cluster 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
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.
|||Peter J. Rousseeuw (1987). “Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis”. Computational and Applied Mathematics 20: 53-65.|
|||Wikipedia entry on the Silhouette Coefficient|
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