sklearn.metrics.davies_bouldin_score¶
- sklearn.metrics.davies_bouldin_score(X, labels)[source]¶
Compute the Davies-Bouldin score.
The score is defined as the average similarity measure of each cluster with its most similar cluster, where similarity is the ratio of within-cluster distances to between-cluster distances. Thus, clusters which are farther apart and less dispersed will result in a better score.
The minimum score is zero, with lower values indicating better clustering.
Read more in the User Guide.
New in version 0.20.
- Parameters
- Xarray-like of shape (n_samples, n_features)
A list of
n_features-dimensional data points. Each row corresponds to a single data point.- labelsarray-like of shape (n_samples,)
Predicted labels for each sample.
- Returns
- score: float
The resulting Davies-Bouldin score.
References
- 1
Davies, David L.; Bouldin, Donald W. (1979). “A Cluster Separation Measure”. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-1 (2): 224-227