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Computes best specificity where sensitivity is >= specified value.
Inherits From: Metric
tfma.metrics.SpecificityAtSensitivity(
sensitivity: float,
num_thresholds: Optional[int] = None,
class_id: Optional[int] = None,
name: Optional[str] = None,
top_k: Optional[int] = None
)
Sensitivity
measures the proportion of actual positives that are correctly
identified as such (tp / (tp + fn)).
Specificity
measures the proportion of actual negatives that are correctly
identified as such (tn / (tn + fp)).
The threshold for the given sensitivity value is computed and used to evaluate the corresponding specificity.
If sample_weight
is None
, weights default to 1.
Use sample_weight
of 0 to mask values.
For additional information about specificity and sensitivity, see the following.
Methods
computations
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
@classmethod
from_config( config: Dict[str, Any] ) -> 'Metric'
get_config
get_config() -> Dict[str, Any]
Returns serializable config.