Rand¶
Rand Index.
The Rand Index 1 2 is a measure of the similarity between two data clusterings. Given a set of elements S
and two partitions of S
to compare, X
and Y
, define the following:
-
a, the number of pairs of elements in
S
that are in the same subset inX
and in the same subset inY
-
b, the number of pairs of elements in
S
that are in the different subset inX
and in different subsets inY
-
c, the number of pairs of elements in
S
that are in the same subset inX
and in different subsets inY
-
d, the number of pairs of elements in
S
that are in the different subset inX
and in the same subset inY
The Rand index, R, is
Parameters¶
-
cm
Default →
None
This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.
Attributes¶
-
bigger_is_better
Indicate if a high value is better than a low one or not.
-
requires_labels
Indicates if labels are required, rather than probabilities.
-
works_with_weights
Indicate whether the model takes into consideration the effect of sample weights
Examples¶
from river import metrics
y_true = [0, 0, 0, 1, 1, 1]
y_pred = [0, 0, 1, 1, 2, 2]
metric = metrics.Rand()
for yt, yp in zip(y_true, y_pred):
metric.update(yt, yp)
metric
Rand: 0.666667
Methods¶
get
Return the current value of the metric.
is_better_than
Indicate if the current metric is better than another one.
Parameters
- other
revert
Revert the metric.
Parameters
- y_true
- y_pred
- w — defaults to
1.0
update
Update the metric.
Parameters
- y_true
- y_pred
- w — defaults to
1.0
works_with
Indicates whether or not a metric can work with a given model.
Parameters
- model — 'base.Estimator'
-
Wikipedia contributors. (2021, January 13). Rand index. In Wikipedia, The Free Encyclopedia, from https://en.wikipedia.org/w/index.php?title=Rand_index&oldid=1000098911 ↩
-
W. M. Rand (1971). "Objective criteria for the evaluation of clustering methods". Journal of the American Statistical Association. American Statistical Association. 66 (336): 846–850. arXiv:1704.01036. doi:10.2307/2284239. JSTOR 2284239. ↩