Cloud AutoML V1beta1 API - Class Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics (v0.8.0)

Reference documentation and code samples for the Cloud AutoML V1beta1 API class Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics.

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

Inherits

  • Object

Extended By

  • Google::Protobuf::MessageExts::ClassMethods

Includes

  • Google::Protobuf::MessageExts

Methods

#annotation_spec_id

def annotation_spec_id() -> ::Array<::String>
Returns
  • (::Array<::String>) — Output only. The annotation spec ids used for this evaluation.

#annotation_spec_id=

def annotation_spec_id=(value) -> ::Array<::String>
Parameter
  • value (::Array<::String>) — Output only. The annotation spec ids used for this evaluation.
Returns
  • (::Array<::String>) — Output only. The annotation spec ids used for this evaluation.

#au_prc

def au_prc() -> ::Float
Returns
  • (::Float) — Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.

#au_prc=

def au_prc=(value) -> ::Float
Parameter
  • value (::Float) — Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.
Returns
  • (::Float) — Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.

#au_roc

def au_roc() -> ::Float
Returns
  • (::Float) — Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.

#au_roc=

def au_roc=(value) -> ::Float
Parameter
  • value (::Float) — Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.
Returns
  • (::Float) — Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.

#base_au_prc

def base_au_prc() -> ::Float
Returns
  • (::Float) — Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.

#base_au_prc=

def base_au_prc=(value) -> ::Float
Parameter
  • value (::Float) — Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.
Returns
  • (::Float) — Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.

#confidence_metrics_entry

def confidence_metrics_entry() -> ::Array<::Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics::ConfidenceMetricsEntry>
Returns
  • (::Array<::Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics::ConfidenceMetricsEntry>) — 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.

#confidence_metrics_entry=

def confidence_metrics_entry=(value) -> ::Array<::Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics::ConfidenceMetricsEntry>
Parameter
  • value (::Array<::Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics::ConfidenceMetricsEntry>) — 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.
Returns
  • (::Array<::Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics::ConfidenceMetricsEntry>) — 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.

#confusion_matrix

def confusion_matrix() -> ::Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics::ConfusionMatrix
Returns

#confusion_matrix=

def confusion_matrix=(value) -> ::Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics::ConfusionMatrix
Parameter
Returns

#log_loss

def log_loss() -> ::Float
Returns
  • (::Float) — Output only. The Log Loss metric.

#log_loss=

def log_loss=(value) -> ::Float
Parameter
  • value (::Float) — Output only. The Log Loss metric.
Returns
  • (::Float) — Output only. The Log Loss metric.