Mean Squared Error

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

# mean-squared-error

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

## When to invoke this skill

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

## Reference signature

```python
from sklearn.metrics import mean_squared_error

# mean_squared_error(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average')
```

## Library docstring

```
Mean squared error regression loss.

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

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'} or array-like of shape             (n_outputs,), default='uniform_average'
    Defines aggregating of multiple output values.
    Array-like value defines weights used to average errors.

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

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

Returns
-------
loss : float or array of floats
    A non-negative floating point value (the best value is 0.0), or an
    array of floating point values, one for each individual target.

Examples
--------
>>> from sklearn.metrics import mean_squared_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_squared_error(y_true, y_pred)
0.375
>>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
>>> y_pred = [[0, 2],[-1, 2],[8, -5]]
>>> mean_squared_error(y_true, y_pred)
0.708...
>>> mean_squared_error(y_true, y_pred, multioutput='raw_values')
array([0.41666667, 1.        ])
>>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
0.825...
```

## Quick recipe

```python
import sklearn.metrics as _m
score = _m.mean_squared_error(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)`.