sklearn.metrics.mutual_info_score(labels_true, labels_pred, *, contingency=None)[source]

Mutual Information between two clusterings.

The Mutual Information is a measure of the similarity between two labels of the same data. Where \(|U_i|\) is the number of the samples in cluster \(U_i\) and \(|V_j|\) is the number of the samples in cluster \(V_j\), the Mutual Information between clusterings \(U\) and \(V\) is given as:

\[MI(U,V)=\sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \frac{|U_i\cap V_j|}{N} \log\frac{N|U_i \cap V_j|}{|U_i||V_j|}\]

This metric is independent of the absolute values of the labels: a permutation of the class or cluster label values won’t change the score value in any way.

This metric is furthermore symmetric: switching label_true with label_pred will return the same score value. This can be useful to measure the agreement of two independent label assignments strategies on the same dataset when the real ground truth is not known.

Read more in the User Guide.

labels_trueint array, shape = [n_samples]

A clustering of the data into disjoint subsets.

labels_predint array-like of shape (n_samples,)

A clustering of the data into disjoint subsets.

contingency{ndarray, sparse matrix} of shape (n_classes_true, n_classes_pred), default=None

A contingency matrix given by the contingency_matrix function. If value is None, it will be computed, otherwise the given value is used, with labels_true and labels_pred ignored.


Mutual information, a non-negative value

See also


Adjusted against chance Mutual Information.


Normalized Mutual Information.


The logarithm used is the natural logarithm (base-e).

Examples using sklearn.metrics.mutual_info_score