# mean_absolute_error

### cuml.metrics.mean_absolute_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average')[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/metrics/regression.py#L248)

Mean absolute error regression loss

Be careful when using this metric with float32 inputs as the result can be
slightly incorrect because of floating point precision if the input is
large enough. float64 will have lower numerical error.

* **Parameters:**
  **y_true**
  : or (n_samples, n_outputs)
    Ground truth (correct) target values.

  **y_pred**
  : or (n_samples, n_outputs)
    Estimated target values.

  **sample_weight**
  : Sample weights.

  **multioutput**
  : 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 in case of multioutput input.
    ‘uniform_average’ :
    Errors of all outputs are averaged with uniform weight.
* **Returns:**
  **loss**
  : 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.
    <br/>
    MAE output is non-negative floating point. The best value is 0.0.

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