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

sklearn.metrics.rand_score(labels_true, labels_pred)

  Rand index.

  The Rand Index computes a similarity measure between two clusterings by considering all pairs of samples and counting pairs that are assigned in the same or different clusters in the predicted and true clusterings [1] [2].

  The raw RI score [3] is:

  > RI = (number of agreeing pairs) / (number of pairs)

  Read more in the User Guide.

  Parameters:
    labels_true array-like of shape (n_samples,), dtype=integral

      Ground truth class labels to be used as a reference.

    labels_pred array-like of shape (n_samples,), dtype=integral

      Cluster labels to evaluate.

  Returns:
    RI float

      Similarity score between 0.0 and 1.0, inclusive, 1.0 stands for perfect match.

  See also

  `adjusted_rand_score`

    Adjusted Rand Score.

  `adjusted_mutual_info_score`

    Adjusted Mutual Information.

  References

  [1]

  Hubert, L., Arabie, P. “Comparing partitions.” Journal of Classification 2, 193–218 (1985)..

  [2]

  Wikipedia: Simple Matching Coefficient

  [3]

  Wikipedia: Rand Index

  Examples

  Perfectly matching labelings have a score of 1 even

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

  Labelings that assign all classes members to the same clusters are complete but may not always be pure, hence penalized:

      >>> rand_score([0, 0, 1, 2], [0, 0, 1, 1])
      0.83...

## Examples using `sklearn.metrics.rand_score`

Adjustment for chance in clustering performance evaluation

Adjustment for chance in clustering performance evaluation