dask_ml.metrics.mean_squared_log_error
dask_ml.metrics.mean_squared_log_error¶
- dask_ml.metrics.mean_squared_log_error(y_true: dask_ml._typing.ArrayLike, y_pred: dask_ml._typing.ArrayLike, sample_weight: Optional[dask_ml._typing.ArrayLike] = None, multioutput: Optional[str] = 'uniform_average', compute: bool = True) dask_ml._typing.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_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.
- squaredbool, default=True (Not supported in Dask)
If True returns MSLE (mean squared log error) value. If False returns RMSLE (root mean squared log error) value.
Deprecated since version 1.4: squared is deprecated in 1.4 and will be removed in 1.6. Use
root_mean_squared_log_error()instead to calculate the root mean squared logarithmic error.
- 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) np.float64(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) np.float64(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]) np.float64(0.060...)