# tfma.evaluators.MetricsAndPlotsEvaluator

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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/evaluators/legacy_metrics_and_plots_evaluator.py#L32-L76) |

Creates an Evaluator for evaluating metrics and plots.

    tfma.evaluators.MetricsAndPlotsEvaluator(
        eval_shared_model: https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/types/EvalSharedModel,
        desired_batch_size: Optional[int] = None,
        metrics_key: str = constants.METRICS_KEY,
        plots_key: str = constants.PLOTS_KEY,
        run_after: str = slice_key_extractor.SLICE_KEY_EXTRACTOR_STAGE_NAME,
        compute_confidence_intervals: Optional[bool] = False,
        min_slice_size: int = 1,
        serialize=False,
        random_seed_for_testing: Optional[int] = None
    ) -> https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/evaluators/Evaluator

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| ## Args ||
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| `eval_shared_model` | Shared model parameters for EvalSavedModel. |
| `desired_batch_size` | Optional batch size for batching in Aggregate. |
| `metrics_key` | Name to use for metrics key in Evaluation output. |
| `plots_key` | Name to use for plots key in Evaluation output. |
| `run_after` | Extractor to run after (None means before any extractors). |
| `compute_confidence_intervals` | Whether or not to compute confidence intervals. |
| `min_slice_size` | If the number of examples in a specific slice is less than min_slice_size, then an error will be returned for that slice. This will be useful to ensure privacy by not displaying the aggregated data for smaller number of examples. |
| `serialize` | If true, serialize the metrics to protos as part of the evaluation as well. |
| `random_seed_for_testing` | Provide for deterministic tests only. |

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| ## Returns ||
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| Evaluator for evaluating metrics and plots. The output will be stored under 'metrics' and 'plots' keys. ||

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