Cloud AutoML V1beta1 API - Class Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics (v0.7.0)

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

(::Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics::ConfusionMatrix) — 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.

#confusion_matrix=

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

Parameter

value (::Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics::ConfusionMatrix) — 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.

Returns

(::Google::Cloud::AutoML::V1beta1::ClassificationEvaluationMetrics::ConfusionMatrix) — 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.

#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.