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_true : array-like of shape = (n_samples) or (n_samples, n_outputs) Ground truth (correct) target values. y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs) Estimated target values. sample_weight : array-like of shape = (n_samples), optional Sample weights. multioutput : string 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:

loss : float 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([ 0.004...,  0.083...])
>>> mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
... 
0.060...