# tfma.metrics.default_regression_specs

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/model-analysis/blob/v0.46.0/tensorflow_model_analysis/metrics/metric_specs.py#L333-L376) |

Returns default metric specs for for regression problems.

    tfma.metrics.default_regression_specs(
        model_names: Optional[List[str]] = None,
        output_names: Optional[List[str]] = None,
        output_weights: Optional[Dict[str, float]] = None,
        loss_functions: Optional[List[Union[tf_keras.metrics.Metric, tf_keras.losses.Loss]]] = None,
        min_value: Optional[float] = None,
        max_value: Optional[float] = None
    ) -> List[https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/MetricsSpec]

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| ## Args ||
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| `model_names` | Optional model names (if multi-model evaluation). |
| `output_names` | Optional list of output names (if multi-output model). |
| `output_weights` | Optional output weights for creating overall metric aggregated across outputs (if multi-output model). If a weight is not provided for an output, it's weight defaults to 0.0 (i.e. output ignored). |
| `loss_functions` | Loss functions to use (if None MSE is used). |
| `min_value` | Min value for calibration plot (if None no plot will be created). |
| `max_value` | Max value for calibration plot (if None no plot will be created). |

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