Compute the rand_score metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute rand_score, or asks how to score with rand_score.
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---
name: rand-score
description: Compute the rand_score metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute rand_score, or asks how to score with rand_score.
metadata:
skill_kind: metric
source_lib: scikit-learn
import_path: sklearn.metrics.rand_score
source: library_introspection
---
# rand-score
> Metric `rand_score` from `scikit-learn` (sklearn.metrics.rand_score)
## When to invoke this skill
The user has predictions + ground truth and asks to evaluate with rand_score, or
mentions `sklearn.metrics.rand_score` directly, or wants the standard scikit-learn implementation.
## Reference signature
```python
from sklearn.metrics import rand_score
# rand_score(labels_true, labels_pred)
```
## Library docstring
```
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:
.. code-block:: text
RI = (number of agreeing pairs) / (number of pairs)
Read more in the :ref:`User Guide <rand_score>`.
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] :doi:`Hubert, L., Arabie, P. "Comparing partitions."
Journal of Classification 2, 193–218 (1985).
<10.1007/BF01908075>`.
.. [2] `Wikipedia: Simple Matching Coefficient
<https://en.wikipedia.org/wiki/Simple_matching_coefficient>`_
.. [3] `Wikipedia: Rand Index <https://en.wikipedia.org/wiki/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
```
## Quick recipe
```python
import sklearn.metrics as _m
score = _m.rand_score(y_true, y_pred)
```
## Don'ts
- Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
- Always check the library version's argument order — sklearn is `(y_true, y_pred)` while torchmetrics is `(preds, target)`.
