R2 Score

---
name: r2-score
description: Compute the r2_score metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute r2_score, or asks how to score with r2_score.
metadata:
  skill_kind: metric
  source_lib: scikit-learn
  import_path: sklearn.metrics.r2_score
  source: library_introspection
---

# r2-score

> Metric `r2_score` from `scikit-learn` (sklearn.metrics.r2_score)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with r2_score, or
mentions `sklearn.metrics.r2_score` directly, or wants the standard scikit-learn implementation.

## Reference signature

```python
from sklearn.metrics import r2_score

# r2_score(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average', force_finite=True)
```

## Library docstring

```
:math:`R^2` (coefficient of determination) regression score function.

Best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). In the general case when the true y is
non-constant, a constant model that always predicts the average y
disregarding the input features would get a :math:`R^2` score of 0.0.

In the particular case when ``y_true`` is constant, the :math:`R^2` score
is not finite: it is either ``NaN`` (perfect predictions) or ``-Inf``
(imperfect predictions). To prevent such non-finite numbers to pollute
higher-level experiments such as a grid search cross-validation, by default
these cases are replaced with 1.0 (perfect predictions) or 0.0 (imperfect
predictions) respectively. You can set ``force_finite`` to ``False`` to
prevent this fix from happening.

Note: when the prediction residuals have zero mean, the :math:`R^2` score
is identical to the
:func:`Explained Variance score <explained_variance_score>`.

Read more in the :ref:`User Guide <r2_score>`.

Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
    Ground truth (correct) target values.

y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
    Estimated target values.

sample_weight : array-like of shape (n_samples,), default=None
    Sample weights.

multioutput : {'raw_values', 'uniform_average', 'variance_weighted'},             array-like of shape (n_outputs,) or None, default='uniform_average'

    Defines aggregating of multiple output scores.
    Array-like value defines weights used to average scores.
    Default is "uniform_average".

    'raw_values' :
        Returns a full set of scores in case of multioutput input.

    'uniform_average' :
        Scores of all outputs are averaged with uniform weight.

    'variance_weighted' :
        Scores of all outputs are averaged, weighted by the variances
        of each individual output.

    .. versionchanged:: 0.19
        Default value of multioutput is 'uniform_average'.

force_finite : bool, default=True
    Flag indicating if ``NaN`` and ``-Inf`` scores resulting from constant
    data should be replaced with real numbers (``1.0`` if predi
```

## Quick recipe

```python
import sklearn.metrics as _m
score = _m.r2_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)`.