Interface ClassificationProto.ClassificationEvaluationMetricsOrBuilder (2.2.1)

Version 2.2.1

• 2.54.0 (latest)
• 2.53.0
• 2.52.0
• 2.51.0
• 2.49.0
• 2.48.0
• 2.47.0
• 2.46.0
• 2.45.0
• 2.44.0
• 2.43.0
• 2.42.0
• 2.41.0
• 2.40.0
• 2.39.0
• 2.37.0
• 2.36.0
• 2.35.0
• 2.34.0
• 2.33.0
• 2.32.0
• 2.31.0
• 2.30.0
• 2.29.0
• 2.28.0
• 2.27.0
• 2.24.0
• 2.23.0
• 2.22.0
• 2.21.0
• 2.20.0
• 2.19.0
• 2.18.0
• 2.17.0
• 2.16.0
• 2.15.0
• 2.14.0
• 2.13.0
• 2.12.0
• 2.11.0
• 2.10.0
• 2.9.0
• 2.8.0
• 2.7.0
• 2.6.0
• 2.5.0
• 2.4.0
• 2.3.18
• 2.2.3
• 2.1.23

public static interface ClassificationProto.ClassificationEvaluationMetricsOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getAnnotationSpecId(int index)

public abstract String getAnnotationSpecId(int index)

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

repeated string annotation_spec_id = 5;

Parameter

Name	Description
index	int The index of the element to return.

Returns

Type	Description
String	The annotationSpecId at the given index.

getAnnotationSpecIdBytes(int index)

public abstract ByteString getAnnotationSpecIdBytes(int index)

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

repeated string annotation_spec_id = 5;

Parameter

Name	Description
index	int The index of the value to return.

Returns

Type	Description
ByteString	The bytes of the annotationSpecId at the given index.

getAnnotationSpecIdCount()

public abstract int getAnnotationSpecIdCount()

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

repeated string annotation_spec_id = 5;

Returns

Type	Description
int	The count of annotationSpecId.

getAnnotationSpecIdList()

public abstract List<String> getAnnotationSpecIdList()

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

repeated string annotation_spec_id = 5;

Returns

Type	Description
List<String>	A list containing the annotationSpecId.

getAuPrc()

public abstract float getAuPrc()

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

float au_prc = 1;

Returns

Type	Description
float	The auPrc.

getAuRoc()

public abstract float getAuRoc()

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

float au_roc = 6;

Returns

Type	Description
float	The auRoc.

getBaseAuPrc() (deprecated)

public abstract float getBaseAuPrc()

Deprecated. google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.base_au_prc is deprecated. See google/cloud/automl/v1beta1/classification.proto;l=189

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

Type	Description
float	The baseAuPrc.

getConfidenceMetricsEntry(int index)

public abstract 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

Name	Description
index	int

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

getConfidenceMetricsEntryCount()

public abstract 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

Type	Description
int

getConfidenceMetricsEntryList()

public abstract 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

Type	Description
List<ConfidenceMetricsEntry>

getConfidenceMetricsEntryOrBuilder(int index)

public abstract 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

Name	Description
index	int

Returns

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder

getConfidenceMetricsEntryOrBuilderList()

public abstract 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

Type	Description
List<? extends com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder>

getConfusionMatrix()

public abstract 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

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix	The confusionMatrix.

getConfusionMatrixOrBuilder()

public abstract 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

Type	Description
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrixOrBuilder

getLogLoss()

public abstract float getLogLoss()

Output only. The Log Loss metric.

float log_loss = 7;

Returns

Type	Description
float	The logLoss.

hasConfusionMatrix()

public abstract 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

Type	Description
boolean	Whether the confusionMatrix field is set.