mean_squared_log_error#

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 theUser 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 multioutputformat.

‘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 anarray of floating point values, one for each individual target.

Examples

>>>fromsklearn.metricsimportmean_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...