sklearn.metrics.fowlkes_mallows_score(labels_true, labels_pred, sparse=False)
Measure the similarity of two clusterings of a set of points.
The Fowlkes-Mallows index (FMI) is defined as the geometric mean between of the precision and recall:
FMI = TP / sqrt((TP + FP) * (TP + FN))
TP is the number of True Positive (i.e. the number of pair of points that belongs in the same clusters in both
FP is the number of False Positive (i.e. the number of pair of points that belongs in the same clusters in
labels_true and not in
FN is the number of False Negative (i.e the number of pair of points that belongs in the same clusters in
labels_pred and not in
The score ranges from 0 to 1. A high value indicates a good similarity between two clusters.
Read more in the User Guide.
|||E. B. Fowkles and C. L. Mallows, 1983. “A method for comparing two hierarchical clusterings”. Journal of the American Statistical Association|
|||Wikipedia entry for the Fowlkes-Mallows Index|
Perfect labelings are both homogeneous and complete, hence have score 1.0:
>>> from sklearn.metrics.cluster import fowlkes_mallows_score >>> fowlkes_mallows_score([0, 0, 1, 1], [0, 0, 1, 1]) 1.0 >>> fowlkes_mallows_score([0, 0, 1, 1], [1, 1, 0, 0]) 1.0
If classes members are completely split across different clusters, the assignment is totally random, hence the FMI is null:
>>> fowlkes_mallows_score([0, 0, 0, 0], [0, 1, 2, 3]) 0.0
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