Packages

c

org.tensorflow.metadata.v0

FalseNegativeRateAtThreshold

final class FalseNegativeRateAtThreshold extends GeneratedMessageV3 with FalseNegativeRateAtThresholdOrBuilder

Protobuf type tensorflow.metadata.v0.FalseNegativeRateAtThreshold

Source
FalseNegativeRateAtThreshold.java
Linear Supertypes
FalseNegativeRateAtThresholdOrBuilder, GeneratedMessageV3, Serializable, AbstractMessage, Message, MessageOrBuilder, AbstractMessageLite[MessageType, BuilderType], MessageLite, MessageLiteOrBuilder, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. FalseNegativeRateAtThreshold
  2. FalseNegativeRateAtThresholdOrBuilder
  3. GeneratedMessageV3
  4. Serializable
  5. AbstractMessage
  6. Message
  7. MessageOrBuilder
  8. AbstractMessageLite
  9. MessageLite
  10. MessageLiteOrBuilder
  11. AnyRef
  12. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. Protected

Value Members

  1. def equals(obj: AnyRef): Boolean
    Definition Classes
    FalseNegativeRateAtThreshold → AbstractMessage → Message → AnyRef → Any
    Annotations
    @Override()
  2. def findInitializationErrors(): List[String]
    Definition Classes
    AbstractMessage → MessageOrBuilder
  3. def getAllFields(): Map[FieldDescriptor, AnyRef]
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  4. def getDefaultInstanceForType(): FalseNegativeRateAtThreshold
    Definition Classes
    FalseNegativeRateAtThreshold → MessageOrBuilder → MessageLiteOrBuilder
    Annotations
    @Override()
  5. def getDescriptorForType(): Descriptor
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  6. def getField(field: FieldDescriptor): AnyRef
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  7. def getInitializationErrorString(): String
    Definition Classes
    AbstractMessage → MessageOrBuilder
  8. def getOneofFieldDescriptor(oneof: OneofDescriptor): FieldDescriptor
    Definition Classes
    GeneratedMessageV3 → AbstractMessage → MessageOrBuilder
  9. def getParserForType(): Parser[FalseNegativeRateAtThreshold]
    Definition Classes
    FalseNegativeRateAtThreshold → GeneratedMessageV3 → Message → MessageLite
    Annotations
    @Override()
  10. def getRepeatedField(field: FieldDescriptor, index: Int): AnyRef
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  11. def getRepeatedFieldCount(field: FieldDescriptor): Int
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  12. def getSerializedSize(): Int
    Definition Classes
    FalseNegativeRateAtThreshold → GeneratedMessageV3 → AbstractMessage → MessageLite
    Annotations
    @Override()
  13. 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.

    Definition Classes
    FalseNegativeRateAtThreshold → FalseNegativeRateAtThresholdOrBuilder
    Annotations
    @Override()
  14. 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;

    Definition Classes
    FalseNegativeRateAtThreshold → FalseNegativeRateAtThresholdOrBuilder
    Annotations
    @Override()
  15. def getUnknownFields(): UnknownFieldSet
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  16. def hasField(field: FieldDescriptor): Boolean
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  17. def hasOneof(oneof: OneofDescriptor): Boolean
    Definition Classes
    GeneratedMessageV3 → AbstractMessage → MessageOrBuilder
  18. 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.

    Definition Classes
    FalseNegativeRateAtThreshold → FalseNegativeRateAtThresholdOrBuilder
    Annotations
    @Override()
  19. def hashCode(): Int
    Definition Classes
    FalseNegativeRateAtThreshold → AbstractMessage → Message → AnyRef → Any
    Annotations
    @Override()
  20. final def isInitialized(): Boolean
    Definition Classes
    FalseNegativeRateAtThreshold → GeneratedMessageV3 → AbstractMessage → MessageLiteOrBuilder
    Annotations
    @Override()
  21. def newBuilderForType(): Builder
    Definition Classes
    FalseNegativeRateAtThreshold → Message → MessageLite
    Annotations
    @Override()
  22. def toBuilder(): Builder
    Definition Classes
    FalseNegativeRateAtThreshold → Message → MessageLite
    Annotations
    @Override()
  23. def toByteArray(): Array[Byte]
    Definition Classes
    AbstractMessageLite → MessageLite
  24. def toByteString(): ByteString
    Definition Classes
    AbstractMessageLite → MessageLite
  25. final def toString(): String
    Definition Classes
    AbstractMessage → Message → AnyRef → Any
  26. def writeDelimitedTo(output: OutputStream): Unit
    Definition Classes
    AbstractMessageLite → MessageLite
    Annotations
    @throws(classOf[java.io.IOException])
  27. def writeTo(output: CodedOutputStream): Unit
    Definition Classes
    FalseNegativeRateAtThreshold → GeneratedMessageV3 → AbstractMessage → MessageLite
    Annotations
    @Override()
  28. def writeTo(output: OutputStream): Unit
    Definition Classes
    AbstractMessageLite → MessageLite
    Annotations
    @throws(classOf[java.io.IOException])