# tfma.metrics.default_binary_classification_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#L379-L426) |

Returns default metric specs for binary classification problems.

    tfma.metrics.default_binary_classification_specs(
        model_names: Optional[List[str]] = None,
        output_names: Optional[List[str]] = None,
        output_weights: Optional[Dict[str, float]] = None,
        binarize: Optional[https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/BinarizationOptions] = None,
        aggregate: Optional[https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/AggregationOptions] = None,
        include_loss: bool = True
    ) -> 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). |
| `binarize` | Optional settings for binarizing multi-class/multi-label metrics. |
| `aggregate` | Optional settings for aggregating multi-class/multi-label metrics. |
| `include_loss` | True to include loss. |

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