# Jaccard Score

> Compute the jaccard_score metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute jaccard_score, or asks how to score with jaccard_score.

- Skill: `qhjqhj00/jaccard-score` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/jaccard-score`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/jaccard-score/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/jaccard-score

---


# jaccard-score

> Metric `jaccard_score` from `scikit-learn` (sklearn.metrics.jaccard_score)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with jaccard_score, or
mentions `sklearn.metrics.jaccard_score` directly, or wants the standard scikit-learn implementation.

## Reference signature

```python
from sklearn.metrics import jaccard_score

# jaccard_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')
```

## Library docstring

```
Jaccard similarity coefficient score.

The Jaccard index [1], or Jaccard similarity coefficient, defined as
the size of the intersection divided by the size of the union of two label
sets, is used to compare set of predicted labels for a sample to the
corresponding set of labels in ``y_true``.

Support beyond :term:`binary` targets is achieved by treating :term:`multiclass`
and :term:`multilabel` data as a collection of binary problems, one for each
label. For the :term:`binary` case, setting `average='binary'` will return the
Jaccard similarity coefficient for `pos_label`. If `average` is not `'binary'`,
`pos_label` is ignored and scores for both classes are computed, then averaged or
both returned (when `average=None`). Similarly, for :term:`multiclass` and
:term:`multilabel` targets, scores for all `labels` are either returned or
averaged depending on the `average` parameter. Use `labels` specify the set of
labels to calculate the score for.

Read more in the :ref:`User Guide <jaccard_similarity_score>`.

Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
    Ground truth (correct) labels. Sparse matrix is only supported when
    labels are of :term:`multilabel` type.

y_pred : 1d array-like, or label indicator array / sparse matrix
    Predicted labels, as returned by a classifier. Sparse matrix is only
    supported when labels are of :term:`multilabel` type.

labels : array-like of shape (n_classes,), default=None
    The set of labels to include when `average != 'binary'`, and their
    order if `average is None`. Labels present in the data can be
    excluded, for example in multiclass classification to exclude a "negative
    class". Labels not present in the data can be included and will be
    "assigned" 0 samples. For multilabel targets, labels are column indices.
    By default, all labels in `y_true` and `y_pred` are used in sorted order.

pos_label : int, float, bool or str, default=1
    The class to report if `average='binary'` and the data is binary,
    otherwise this parameter is ignored.
    For multiclass or multilabel targets, set `labels=[pos_label]` and
    `average != 'binary'` to report metrics for o
```

## Quick recipe

```python
import sklearn.metrics as _m
score = _m.jaccard_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)`.