tfma.metrics.RelativeCoefficientOfDiscrimination
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Relative coefficient of discrimination metric.
Inherits From: Metric
tfma.metrics.RelativeCoefficientOfDiscrimination(
name: str = RELATIVE_COEFFICIENT_OF_DISCRIMINATION_NAME
)
The relative coefficient of discrimination measures the ratio between the
average prediction for the positive examples and the average prediction for
the negative examples. This has a very simple intuitive explanation, measuring
how much higher is the prediction going to be for a positive example than for
a negative example.
Attributes |
compute_confidence_interval
|
Whether to compute confidence intervals for this metric.
Note that this may not completely remove the computational overhead
involved in computing a given metric. This is only respected by the
jackknife confidence interval method.
|
Methods
computations
View source
computations(
eval_config: Optional[tfma.EvalConfig
] = None,
schema: Optional[schema_pb2.Schema] = None,
model_names: Optional[List[str]] = None,
output_names: Optional[List[str]] = None,
sub_keys: Optional[List[Optional[SubKey]]] = None,
aggregation_type: Optional[AggregationType] = None,
class_weights: Optional[Dict[int, float]] = None,
example_weighted: bool = False,
query_key: Optional[str] = None
) -> tfma.metrics.MetricComputations
Creates computations associated with metric.
from_config
View source
@classmethod
from_config(
config: Dict[str, Any]
) -> 'Metric'
get_config
View source
get_config() -> Dict[str, Any]
Returns serializable config.
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tfma.metrics.RelativeCoefficientOfDiscrimination\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/model-analysis/blob/v0.46.0/tensorflow_model_analysis/metrics/tjur_discrimination.py#L111-L130) |\n\nRelative coefficient of discrimination metric.\n\nInherits From: [`Metric`](../../tfma/metrics/Metric) \n\n tfma.metrics.RelativeCoefficientOfDiscrimination(\n name: str = RELATIVE_COEFFICIENT_OF_DISCRIMINATION_NAME\n )\n\nThe relative coefficient of discrimination measures the ratio between the\naverage prediction for the positive examples and the average prediction for\nthe negative examples. This has a very simple intuitive explanation, measuring\nhow much higher is the prediction going to be for a positive example than for\na negative example.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------|--------------|\n| `name` | Metric name. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|-------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `compute_confidence_interval` | Whether to compute confidence intervals for this metric. \u003cbr /\u003e Note that this may not completely remove the computational overhead involved in computing a given metric. This is only respected by the jackknife confidence interval method. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `computations`\n\n[View source](https://github.com/tensorflow/model-analysis/blob/v0.46.0/tensorflow_model_analysis/metrics/metric_types.py#L862-L888) \n\n computations(\n eval_config: Optional[../../tfma/EvalConfig] = None,\n schema: Optional[schema_pb2.Schema] = None,\n model_names: Optional[List[str]] = None,\n output_names: Optional[List[str]] = None,\n sub_keys: Optional[List[Optional[SubKey]]] = None,\n aggregation_type: Optional[AggregationType] = None,\n class_weights: Optional[Dict[int, float]] = None,\n example_weighted: bool = False,\n query_key: Optional[str] = None\n ) -\u003e ../../tfma/metrics/MetricComputations\n\nCreates computations associated with metric.\n\n### `from_config`\n\n[View source](https://github.com/tensorflow/model-analysis/blob/v0.46.0/tensorflow_model_analysis/metrics/metric_types.py#L842-L847) \n\n @classmethod\n from_config(\n config: Dict[str, Any]\n ) -\u003e 'Metric'\n\n### `get_config`\n\n[View source](https://github.com/tensorflow/model-analysis/blob/v0.46.0/tensorflow_model_analysis/metrics/metric_types.py#L838-L840) \n\n get_config() -\u003e Dict[str, Any]\n\nReturns serializable config."]]