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scikit-learn 1.3.2
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# `sklearn.metrics`.davies_bouldin_score

sklearn.metrics.davies_bouldin_score(X, labels)

  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:
    X array-like of shape (n_samples, n_features)

      A list of `n_features`-dimensional data points. Each row corresponds to a single data point.

    labels array-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