mean_squared_log_error

sklearn.metrics.mean_squared_log_error(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average', squared='deprecated')

Mean squared logarithmic error regression loss.

Read more in the User Guide.

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.

squaredbool, default=True

If True returns MSLE (mean squared log error) value. If False returns RMSLE (root mean squared log error) value.

Deprecated since version 1.4: squared is deprecated in 1.4 and will be removed in 1.6. Use root_mean_squared_log_error instead to calculate the root mean squared logarithmic error.

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 mean_squared_log_error
>>> y_true = [3, 5, 2.5, 7]
>>> y_pred = [2.5, 5, 4, 8]
>>> mean_squared_log_error(y_true, y_pred)
np.float64(0.039...)
>>> y_true = [[0.5, 1], [1, 2], [7, 6]]
>>> y_pred = [[0.5, 2], [1, 2.5], [8, 8]]
>>> mean_squared_log_error(y_true, y_pred)
np.float64(0.044...)
>>> mean_squared_log_error(y_true, y_pred, multioutput='raw_values')
array([0.00462428, 0.08377444])
>>> mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
np.float64(0.060...)