Class ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder (2.5.0)

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public static final class ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder extends GeneratedMessageV3.Builder<ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder> implements ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder

Metrics for a single confidence threshold.

Protobuf type google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Implements

ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns

Type	Description
Descriptor

Methods

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry build()

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

buildPartial()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry buildPartial()

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

clear()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clear()

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearConfidenceThreshold()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearConfidenceThreshold()

Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value.

float confidence_threshold = 1;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearF1Score()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearF1Score()

Output only. The harmonic mean of recall and precision.

float f1_score = 4;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearF1ScoreAt1()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearF1ScoreAt1()

Output only. The harmonic mean of recall_at1 and precision_at1.

float f1_score_at1 = 7;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearFalseNegativeCount()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearFalseNegativeCount()

Output only. The number of ground truth labels that are not matched by a model created label.

int64 false_negative_count = 12;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearFalsePositiveCount()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearFalsePositiveCount()

Output only. The number of model created labels that do not match a ground truth label.

int64 false_positive_count = 11;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearFalsePositiveRate()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearFalsePositiveRate()

Output only. False Positive Rate for the given confidence threshold.

float false_positive_rate = 8;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearFalsePositiveRateAt1()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearFalsePositiveRateAt1()

Output only. The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

float false_positive_rate_at1 = 9;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearField(Descriptors.FieldDescriptor field)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearField(Descriptors.FieldDescriptor field)

Parameter

Name	Description
field	FieldDescriptor

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearOneof(Descriptors.OneofDescriptor oneof)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter

Name	Description
oneof	OneofDescriptor

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clearPositionThreshold()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearPositionThreshold()

Output only. Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold.

int32 position_threshold = 14;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearPrecision()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearPrecision()

Output only. Precision for the given confidence threshold.

float precision = 3;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearPrecisionAt1()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearPrecisionAt1()

Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

float precision_at1 = 6;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearRecall()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearRecall()

Output only. Recall (True Positive Rate) for the given confidence threshold.

float recall = 2;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearRecallAt1()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearRecallAt1()

Output only. The Recall (True Positive Rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

float recall_at1 = 5;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearTrueNegativeCount()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearTrueNegativeCount()

Output only. The number of labels that were not created by the model, but if they would, they would not match a ground truth label.

int64 true_negative_count = 13;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clearTruePositiveCount()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clearTruePositiveCount()

Output only. The number of model created labels that match a ground truth label.

int64 true_positive_count = 10;

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

clone()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder clone()

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getConfidenceThreshold()

public float getConfidenceThreshold()

Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value.

float confidence_threshold = 1;

Returns

Type	Description
float	The confidenceThreshold.

getDefaultInstanceForType()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry getDefaultInstanceForType()

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns

Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getF1Score()

public float getF1Score()

Output only. The harmonic mean of recall and precision.

float f1_score = 4;

Returns

Type	Description
float	The f1Score.

getF1ScoreAt1()

public float getF1ScoreAt1()

Output only. The harmonic mean of recall_at1 and precision_at1.

float f1_score_at1 = 7;

Returns

Type	Description
float	The f1ScoreAt1.

getFalseNegativeCount()

public long getFalseNegativeCount()

Output only. The number of ground truth labels that are not matched by a model created label.

int64 false_negative_count = 12;

Returns

Type	Description
long	The falseNegativeCount.

getFalsePositiveCount()

public long getFalsePositiveCount()

Output only. The number of model created labels that do not match a ground truth label.

int64 false_positive_count = 11;

Returns

Type	Description
long	The falsePositiveCount.

getFalsePositiveRate()

public float getFalsePositiveRate()

Output only. False Positive Rate for the given confidence threshold.

float false_positive_rate = 8;

Returns

Type	Description
float	The falsePositiveRate.

getFalsePositiveRateAt1()

public float getFalsePositiveRateAt1()

Output only. The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

float false_positive_rate_at1 = 9;

Returns

Type	Description
float	The falsePositiveRateAt1.

getPositionThreshold()

public int getPositionThreshold()

Output only. Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold.

int32 position_threshold = 14;

Returns

Type	Description
int	The positionThreshold.

getPrecision()

public float getPrecision()

Output only. Precision for the given confidence threshold.

float precision = 3;

Returns

Type	Description
float	The precision.

getPrecisionAt1()

public float getPrecisionAt1()

Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

float precision_at1 = 6;

Returns

Type	Description
float	The precisionAt1.

getRecall()

public float getRecall()

Output only. Recall (True Positive Rate) for the given confidence threshold.

float recall = 2;

Returns

Type	Description
float	The recall.

getRecallAt1()

public float getRecallAt1()

Output only. The Recall (True Positive Rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

float recall_at1 = 5;

Returns

Type	Description
float	The recallAt1.

getTrueNegativeCount()

public long getTrueNegativeCount()

Output only. The number of labels that were not created by the model, but if they would, they would not match a ground truth label.

int64 true_negative_count = 13;

Returns

Type	Description
long	The trueNegativeCount.

getTruePositiveCount()

public long getTruePositiveCount()

Output only. The number of model created labels that match a ground truth label.

int64 true_positive_count = 10;

Returns

Type	Description
long	The truePositiveCount.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns

Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns

Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeFrom(ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry other)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder mergeFrom(ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry other)

Parameter

Name	Description
other	ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters

Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions

Type	Description
IOException

mergeFrom(Message other)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder mergeFrom(Message other)

Parameter

Name	Description
other	Message

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeUnknownFields(UnknownFieldSet unknownFields)

public final ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

setConfidenceThreshold(float value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setConfidenceThreshold(float value)

Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value.

float confidence_threshold = 1;

Parameter

Name	Description
value	float The confidenceThreshold to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setF1Score(float value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setF1Score(float value)

Output only. The harmonic mean of recall and precision.

float f1_score = 4;

Parameter

Name	Description
value	float The f1Score to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setF1ScoreAt1(float value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setF1ScoreAt1(float value)

Output only. The harmonic mean of recall_at1 and precision_at1.

float f1_score_at1 = 7;

Parameter

Name	Description
value	float The f1ScoreAt1 to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setFalseNegativeCount(long value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setFalseNegativeCount(long value)

Output only. The number of ground truth labels that are not matched by a model created label.

int64 false_negative_count = 12;

Parameter

Name	Description
value	long The falseNegativeCount to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setFalsePositiveCount(long value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setFalsePositiveCount(long value)

Output only. The number of model created labels that do not match a ground truth label.

int64 false_positive_count = 11;

Parameter

Name	Description
value	long The falsePositiveCount to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setFalsePositiveRate(float value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setFalsePositiveRate(float value)

Output only. False Positive Rate for the given confidence threshold.

float false_positive_rate = 8;

Parameter

Name	Description
value	float The falsePositiveRate to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setFalsePositiveRateAt1(float value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setFalsePositiveRateAt1(float value)

Output only. The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

float false_positive_rate_at1 = 9;

Parameter

Name	Description
value	float The falsePositiveRateAt1 to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setField(Descriptors.FieldDescriptor field, Object value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setPositionThreshold(int value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setPositionThreshold(int value)

Output only. Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold.

int32 position_threshold = 14;

Parameter

Name	Description
value	int The positionThreshold to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setPrecision(float value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setPrecision(float value)

Output only. Precision for the given confidence threshold.

float precision = 3;

Parameter

Name	Description
value	float The precision to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setPrecisionAt1(float value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setPrecisionAt1(float value)

Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

float precision_at1 = 6;

Parameter

Name	Description
value	float The precisionAt1 to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setRecall(float value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setRecall(float value)

Output only. Recall (True Positive Rate) for the given confidence threshold.

float recall = 2;

Parameter

Name	Description
value	float The recall to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setRecallAt1(float value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setRecallAt1(float value)

Output only. The Recall (True Positive Rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

float recall_at1 = 5;

Parameter

Name	Description
value	float The recallAt1 to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters

Name	Description
field	FieldDescriptor
index	int
value	Object

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setTrueNegativeCount(long value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setTrueNegativeCount(long value)

Output only. The number of labels that were not created by the model, but if they would, they would not match a ground truth label.

int64 true_negative_count = 13;

Parameter

Name	Description
value	long The trueNegativeCount to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setTruePositiveCount(long value)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setTruePositiveCount(long value)

Output only. The number of model created labels that match a ground truth label.

int64 true_positive_count = 10;

Parameter

Name	Description
value	long The truePositiveCount to set.

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder	This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)