mean_squared_log_error (original) (raw)

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

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.

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) 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) 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]) 0.060...