sklearn.metrics
.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 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), optional
Sample weights.
- multioutputstring in [‘raw_values’, ‘uniform_average’] or array-like of shape = (n_outputs)
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...