# Root Mean Squared Log Error

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

- Skill: `qhjqhj00/root-mean-squared-log-error` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/root-mean-squared-log-error`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/root-mean-squared-log-error/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/root-mean-squared-log-error

---


# root-mean-squared-log-error

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

## When to invoke this skill

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

## Reference signature

```python
from sklearn.metrics import root_mean_squared_log_error

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

## Library docstring

```
Root mean squared logarithmic error regression loss.

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

.. versionadded:: 1.4

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 when the input is of multioutput
        format.

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

Returns
-------
loss : float or ndarray 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 root_mean_squared_log_error
>>> y_true = [3, 5, 2.5, 7]
>>> y_pred = [2.5, 5, 4, 8]
>>> root_mean_squared_log_error(y_true, y_pred)
0.199...
```

## Quick recipe

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