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t

org.tensorflow.metadata.v0

FalsePositiveRateAtThresholdOrBuilder

trait FalsePositiveRateAtThresholdOrBuilder extends MessageOrBuilder

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Inherited
  1. FalsePositiveRateAtThresholdOrBuilder
  2. MessageOrBuilder
  3. MessageLiteOrBuilder
  4. AnyRef
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Abstract Value Members

  1. abstract def findInitializationErrors(): List[String]
    Definition Classes
    MessageOrBuilder
  2. abstract def getAllFields(): Map[FieldDescriptor, AnyRef]
    Definition Classes
    MessageOrBuilder
  3. abstract def getDefaultInstanceForType(): Message
    Definition Classes
    MessageOrBuilder → MessageLiteOrBuilder
  4. abstract def getDescriptorForType(): Descriptor
    Definition Classes
    MessageOrBuilder
  5. abstract def getField(field: FieldDescriptor): AnyRef
    Definition Classes
    MessageOrBuilder
  6. abstract def getInitializationErrorString(): String
    Definition Classes
    MessageOrBuilder
  7. abstract def getOneofFieldDescriptor(oneof: OneofDescriptor): FieldDescriptor
    Definition Classes
    MessageOrBuilder
  8. abstract def getRepeatedField(field: FieldDescriptor, index: Int): AnyRef
    Definition Classes
    MessageOrBuilder
  9. abstract def getRepeatedFieldCount(field: FieldDescriptor): Int
    Definition Classes
    MessageOrBuilder
  10. abstract def getThreshold(): DoubleValue

    Threshold to apply to a prediction to determine positive vs negative.
Note: if the model is calibrated, the threshold can be thought of as a
probability so the threshold has a stable, intuitive semantic.
However, not all solutions may be calibrated, and not all computations of
the metric may operate on a calibrated score. In AutoTFX, the final model
metrics are computed on a calibrated score, but the metrics computed within
the model selection process are uncalibrated. Be aware of this possible
skew in the metrics between model selection and final model evaluation.


    Threshold to apply to a prediction to determine positive vs negative.
Note: if the model is calibrated, the threshold can be thought of as a
probability so the threshold has a stable, intuitive semantic.
However, not all solutions may be calibrated, and not all computations of
the metric may operate on a calibrated score. In AutoTFX, the final model
metrics are computed on a calibrated score, but the metrics computed within
the model selection process are uncalibrated. Be aware of this possible
skew in the metrics between model selection and final model evaluation.

    .google.protobuf.DoubleValue threshold = 1;

    returns

    The threshold.

  11. abstract def getThresholdOrBuilder(): DoubleValueOrBuilder

    Threshold to apply to a prediction to determine positive vs negative.
Note: if the model is calibrated, the threshold can be thought of as a
probability so the threshold has a stable, intuitive semantic.
However, not all solutions may be calibrated, and not all computations of
the metric may operate on a calibrated score. In AutoTFX, the final model
metrics are computed on a calibrated score, but the metrics computed within
the model selection process are uncalibrated. Be aware of this possible
skew in the metrics between model selection and final model evaluation.


    Threshold to apply to a prediction to determine positive vs negative.
Note: if the model is calibrated, the threshold can be thought of as a
probability so the threshold has a stable, intuitive semantic.
However, not all solutions may be calibrated, and not all computations of
the metric may operate on a calibrated score. In AutoTFX, the final model
metrics are computed on a calibrated score, but the metrics computed within
the model selection process are uncalibrated. Be aware of this possible
skew in the metrics between model selection and final model evaluation.

    .google.protobuf.DoubleValue threshold = 1;

  12. abstract def getUnknownFields(): UnknownFieldSet
    Definition Classes
    MessageOrBuilder
  13. abstract def hasField(field: FieldDescriptor): Boolean
    Definition Classes
    MessageOrBuilder
  14. abstract def hasOneof(oneof: OneofDescriptor): Boolean
    Definition Classes
    MessageOrBuilder
  15. abstract def hasThreshold(): Boolean

    Threshold to apply to a prediction to determine positive vs negative.
Note: if the model is calibrated, the threshold can be thought of as a
probability so the threshold has a stable, intuitive semantic.
However, not all solutions may be calibrated, and not all computations of
the metric may operate on a calibrated score. In AutoTFX, the final model
metrics are computed on a calibrated score, but the metrics computed within
the model selection process are uncalibrated. Be aware of this possible
skew in the metrics between model selection and final model evaluation.


    Threshold to apply to a prediction to determine positive vs negative.
Note: if the model is calibrated, the threshold can be thought of as a
probability so the threshold has a stable, intuitive semantic.
However, not all solutions may be calibrated, and not all computations of
the metric may operate on a calibrated score. In AutoTFX, the final model
metrics are computed on a calibrated score, but the metrics computed within
the model selection process are uncalibrated. Be aware of this possible
skew in the metrics between model selection and final model evaluation.

    .google.protobuf.DoubleValue threshold = 1;

    returns

    Whether the threshold field is set.

  16. abstract def isInitialized(): Boolean
    Definition Classes
    MessageLiteOrBuilder