Class ClassificationProto.ClassificationEvaluationMetrics.Builder

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

Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.

Protobuf type google.cloud.automl.v1beta1.ClassificationEvaluationMetrics

Inheritance

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

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
TypeDescription
Descriptor

Methods

addAllAnnotationSpecId(Iterable<String> values)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addAllAnnotationSpecId(Iterable<String> values)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
NameDescription
valuesIterable<String>

The annotationSpecId to add.

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

addAllConfidenceMetricsEntry(Iterable<? extends ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry> values)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addAllConfidenceMetricsEntry(Iterable<? extends ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry> values)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
NameDescription
valuesIterable<? extends com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry>
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

addAnnotationSpecId(String value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addAnnotationSpecId(String value)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
NameDescription
valueString

The annotationSpecId to add.

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

addAnnotationSpecIdBytes(ByteString value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addAnnotationSpecIdBytes(ByteString value)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
NameDescription
valueByteString

The bytes of the annotationSpecId to add.

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

addConfidenceMetricsEntry(ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addConfidenceMetricsEntry(ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
NameDescription
valueClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

addConfidenceMetricsEntry(ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addConfidenceMetricsEntry(ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
NameDescription
builderForValueClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

addConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameters
NameDescription
indexint
valueClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

addConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameters
NameDescription
indexint
builderForValueClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

addConfidenceMetricsEntryBuilder()

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder addConfidenceMetricsEntryBuilder()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

addConfidenceMetricsEntryBuilder(int index)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder addConfidenceMetricsEntryBuilder(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
NameDescription
indexint
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Parameters
NameDescription
fieldFieldDescriptor
valueObject
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder
Overrides

build()

public ClassificationProto.ClassificationEvaluationMetrics build()
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics

buildPartial()

public ClassificationProto.ClassificationEvaluationMetrics buildPartial()
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics

clear()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clear()
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder
Overrides

clearAnnotationSpecId()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearAnnotationSpecId()

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

clearAuPrc()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearAuPrc()

Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.

float au_prc = 1;

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

clearAuRoc()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearAuRoc()

Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.

float au_roc = 6;

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

clearBaseAuPrc()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearBaseAuPrc()

Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.

float base_au_prc = 2 [deprecated = true];

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

clearConfidenceMetricsEntry()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearConfidenceMetricsEntry()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

clearConfusionMatrix()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearConfusionMatrix()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

clearField(Descriptors.FieldDescriptor field)

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearField(Descriptors.FieldDescriptor field)
Parameter
NameDescription
fieldFieldDescriptor
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder
Overrides

clearLogLoss()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearLogLoss()

Output only. The Log Loss metric.

float log_loss = 7;

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public ClassificationProto.ClassificationEvaluationMetrics.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
NameDescription
oneofOneofDescriptor
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder
Overrides

clone()

public ClassificationProto.ClassificationEvaluationMetrics.Builder clone()
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder
Overrides

getAnnotationSpecId(int index)

public String getAnnotationSpecId(int index)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
NameDescription
indexint

The index of the element to return.

Returns
TypeDescription
String

The annotationSpecId at the given index.

getAnnotationSpecIdBytes(int index)

public ByteString getAnnotationSpecIdBytes(int index)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
NameDescription
indexint

The index of the value to return.

Returns
TypeDescription
ByteString

The bytes of the annotationSpecId at the given index.

getAnnotationSpecIdCount()

public int getAnnotationSpecIdCount()

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Returns
TypeDescription
int

The count of annotationSpecId.

getAnnotationSpecIdList()

public ProtocolStringList getAnnotationSpecIdList()

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Returns
TypeDescription
ProtocolStringList

A list containing the annotationSpecId.

getAuPrc()

public float getAuPrc()

Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.

float au_prc = 1;

Returns
TypeDescription
float

The auPrc.

getAuRoc()

public float getAuRoc()

Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.

float au_roc = 6;

Returns
TypeDescription
float

The auRoc.

getBaseAuPrc()

public float getBaseAuPrc()

Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.

float base_au_prc = 2 [deprecated = true];

Returns
TypeDescription
float

The baseAuPrc.

getConfidenceMetricsEntry(int index)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry getConfidenceMetricsEntry(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
NameDescription
indexint
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

getConfidenceMetricsEntryBuilder(int index)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder getConfidenceMetricsEntryBuilder(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
NameDescription
indexint
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

getConfidenceMetricsEntryBuilderList()

public List<ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder> getConfidenceMetricsEntryBuilderList()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
TypeDescription
List<Builder>

getConfidenceMetricsEntryCount()

public int getConfidenceMetricsEntryCount()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
TypeDescription
int

getConfidenceMetricsEntryList()

public List<ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry> getConfidenceMetricsEntryList()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
TypeDescription
List<ConfidenceMetricsEntry>

getConfidenceMetricsEntryOrBuilder(int index)

public ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder getConfidenceMetricsEntryOrBuilder(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
NameDescription
indexint
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder

getConfidenceMetricsEntryOrBuilderList()

public List<? extends ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder> getConfidenceMetricsEntryOrBuilderList()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
TypeDescription
List<? extends com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder>

getConfusionMatrix()

public ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix getConfusionMatrix()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix

The confusionMatrix.

getConfusionMatrixBuilder()

public ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Builder getConfusionMatrixBuilder()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Builder

getConfusionMatrixOrBuilder()

public ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrixOrBuilder getConfusionMatrixOrBuilder()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrixOrBuilder

getDefaultInstanceForType()

public ClassificationProto.ClassificationEvaluationMetrics getDefaultInstanceForType()
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()
Returns
TypeDescription
Descriptor
Overrides

getLogLoss()

public float getLogLoss()

Output only. The Log Loss metric.

float log_loss = 7;

Returns
TypeDescription
float

The logLoss.

hasConfusionMatrix()

public boolean hasConfusionMatrix()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
TypeDescription
boolean

Whether the confusionMatrix field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
TypeDescription
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
TypeDescription
boolean
Overrides

mergeConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder mergeConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix value)

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Parameter
NameDescription
valueClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

mergeFrom(ClassificationProto.ClassificationEvaluationMetrics other)

public ClassificationProto.ClassificationEvaluationMetrics.Builder mergeFrom(ClassificationProto.ClassificationEvaluationMetrics other)
Parameter
NameDescription
otherClassificationProto.ClassificationEvaluationMetrics
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public ClassificationProto.ClassificationEvaluationMetrics.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputCodedInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder
Overrides Exceptions
TypeDescription
IOException

mergeFrom(Message other)

public ClassificationProto.ClassificationEvaluationMetrics.Builder mergeFrom(Message other)
Parameter
NameDescription
otherMessage
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder
Overrides

mergeUnknownFields(UnknownFieldSet unknownFields)

public final ClassificationProto.ClassificationEvaluationMetrics.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
NameDescription
unknownFieldsUnknownFieldSet
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder
Overrides

removeConfidenceMetricsEntry(int index)

public ClassificationProto.ClassificationEvaluationMetrics.Builder removeConfidenceMetricsEntry(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
NameDescription
indexint
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

setAnnotationSpecId(int index, String value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setAnnotationSpecId(int index, String value)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameters
NameDescription
indexint

The index to set the value at.

valueString

The annotationSpecId to set.

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

setAuPrc(float value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setAuPrc(float value)

Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.

float au_prc = 1;

Parameter
NameDescription
valuefloat

The auPrc to set.

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

setAuRoc(float value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setAuRoc(float value)

Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.

float au_roc = 6;

Parameter
NameDescription
valuefloat

The auRoc to set.

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

setBaseAuPrc(float value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setBaseAuPrc(float value)

Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.

float base_au_prc = 2 [deprecated = true];

Parameter
NameDescription
valuefloat

The baseAuPrc to set.

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

setConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry value)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameters
NameDescription
indexint
valueClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

setConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setConfidenceMetricsEntry(int index, ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder builderForValue)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameters
NameDescription
indexint
builderForValueClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

setConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix value)

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Parameter
NameDescription
valueClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

setConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Builder builderForValue)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setConfusionMatrix(ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Builder builderForValue)

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Parameter
NameDescription
builderForValueClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Builder
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

setField(Descriptors.FieldDescriptor field, Object value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setField(Descriptors.FieldDescriptor field, Object value)
Parameters
NameDescription
fieldFieldDescriptor
valueObject
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder
Overrides

setLogLoss(float value)

public ClassificationProto.ClassificationEvaluationMetrics.Builder setLogLoss(float value)

Output only. The Log Loss metric.

float log_loss = 7;

Parameter
NameDescription
valuefloat

The logLoss to set.

Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder

This builder for chaining.

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

public ClassificationProto.ClassificationEvaluationMetrics.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
Parameters
NameDescription
fieldFieldDescriptor
indexint
valueObject
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder
Overrides

setUnknownFields(UnknownFieldSet unknownFields)

public final ClassificationProto.ClassificationEvaluationMetrics.Builder setUnknownFields(UnknownFieldSet unknownFields)
Parameter
NameDescription
unknownFieldsUnknownFieldSet
Returns
TypeDescription
ClassificationProto.ClassificationEvaluationMetrics.Builder
Overrides