root_mean_squared_log_error#

sklearn.metrics.root_mean_squared_log_error(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average')[source]#

Root mean squared logarithmic error regression loss.

Read more in the User Guide.

Added in version 1.4.

Parameters:

y_truearray-like of shape (n_samples,) or (n_samples, n_outputs)

Ground truth (correct) target values.

y_predarray-like of shape (n_samples,) or (n_samples, n_outputs)

Estimated target values.

sample_weightarray-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:

lossfloat 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)
np.float64(0.199...)