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t

org.tensorflow.metadata.v0

FalseNegativeRateAtThresholdOrBuilder

trait FalseNegativeRateAtThresholdOrBuilder extends MessageOrBuilder

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  1. FalseNegativeRateAtThresholdOrBuilder
  2. MessageOrBuilder
  3. MessageLiteOrBuilder
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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

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  9. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  14. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  15. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  16. def toString(): String
    Definition Classes
    AnyRef → Any
  17. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  18. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  19. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()

Inherited from MessageOrBuilder

Inherited from MessageLiteOrBuilder

Inherited from AnyRef

Inherited from Any

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