dask_ml.metrics.mean_squared_log_error¶
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dask_ml.metrics.
mean_squared_log_error
(y_true: ArrayLike, y_pred: ArrayLike, sample_weight: Optional[ArrayLike] = None, multioutput: Optional[str] = 'uniform_average', compute: bool = True) → ArrayLike¶ Mean squared logarithmic error regression loss.
This docstring was copied from sklearn.metrics.mean_squared_log_error.
Some inconsistencies with the Dask version may exist.
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,), 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: - 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 # doctest: +SKIP >>> y_true = [3, 5, 2.5, 7] # doctest: +SKIP >>> y_pred = [2.5, 5, 4, 8] # doctest: +SKIP >>> mean_squared_log_error(y_true, y_pred) # doctest: +SKIP 0.039... >>> y_true = [[0.5, 1], [1, 2], [7, 6]] # doctest: +SKIP >>> y_pred = [[0.5, 2], [1, 2.5], [8, 8]] # doctest: +SKIP >>> mean_squared_log_error(y_true, y_pred) # doctest: +SKIP 0.044... >>> mean_squared_log_error(y_true, y_pred, multioutput='raw_values') # doctest: +SKIP array([0.00462428, 0.08377444]) >>> mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7]) # doctest: +SKIP 0.060...