snowflake.ml.modeling.metrics.mean_absolute_error¶

snowflake.ml.modeling.metrics.mean_absolute_error(*, df: DataFrame, y_true_col_names: Union[str, List[str]], y_pred_col_names: Union[str, List[str]], sample_weight_col_name: Optional[str] = None, multioutput: Union[str, _SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]] = 'uniform_average') → Union[float, ndarray[Any, dtype[float64]]]¶

Mean absolute error regression loss.

Args:

df: snowpark.DataFrame

Input dataframe.

y_true_col_names: string or list of strings

Column name(s) representing actual values.

y_pred_col_names: string or list of strings

Column name(s) representing predicted values.

sample_weight_col_name: string, default=None

Column name representing 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 in case of multioutput input.

‘uniform_average’:

Errors of all outputs are averaged with uniform weight.

Returns:

loss: float or ndarray of floats

If multioutput is ‘raw_values’, then mean absolute error is returned for each output separately. If multioutput is ‘uniform_average’ or an ndarray of weights, then the weighted average of all output errors is returned.

MAE output is non-negative floating point. The best value is 0.0.