Class: Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetrics
- Inherits:
-
Object
- Object
- Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetrics
- Includes:
- Core::Hashable, Core::JsonObjectSupport
- Defined in:
- lib/google/apis/aiplatform_v1beta1/classes.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb
Overview
Metrics for classification evaluation results.
Instance Attribute Summary collapse
-
#au_prc ⇒ Float
The Area Under Precision-Recall Curve metric.
-
#au_roc ⇒ Float
The Area Under Receiver Operating Characteristic curve metric.
-
#confidence_metrics ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics>
Metrics for each
confidenceThresholdin 0.00,0.05,0.10,...,0.95,0.96,0.97,0. -
#confusion_matrix ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsConfusionMatrix
Confusion matrix of the evaluation.
-
#log_loss ⇒ Float
The Log Loss metric.
Instance Method Summary collapse
-
#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetrics
constructor
A new instance of GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetrics.
-
#update!(**args) ⇒ Object
Update properties of this object.
Constructor Details
#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetrics
Returns a new instance of GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetrics.
19987 19988 19989 |
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 19987 def initialize(**args) update!(**args) end |
Instance Attribute Details
#au_prc ⇒ Float
The Area Under Precision-Recall Curve metric. Micro-averaged for the overall
evaluation.
Corresponds to the JSON property auPrc
19960 19961 19962 |
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 19960 def au_prc @au_prc end |
#au_roc ⇒ Float
The Area Under Receiver Operating Characteristic curve metric. Micro-averaged
for the overall evaluation.
Corresponds to the JSON property auRoc
19966 19967 19968 |
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 19966 def au_roc @au_roc end |
#confidence_metrics ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics>
Metrics for each confidenceThreshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.
98,0.99 and positionThreshold = 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
positionThreshold, but from these no aggregated metrics are computed.
Corresponds to the JSON property confidenceMetrics
19975 19976 19977 |
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 19975 def confidence_metrics @confidence_metrics end |
#confusion_matrix ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsConfusionMatrix
Confusion matrix of the evaluation.
Corresponds to the JSON property confusionMatrix
19980 19981 19982 |
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 19980 def confusion_matrix @confusion_matrix end |
#log_loss ⇒ Float
The Log Loss metric.
Corresponds to the JSON property logLoss
19985 19986 19987 |
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 19985 def log_loss @log_loss end |
Instance Method Details
#update!(**args) ⇒ Object
Update properties of this object
19992 19993 19994 19995 19996 19997 19998 |
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 19992 def update!(**args) @au_prc = args[:au_prc] if args.key?(:au_prc) @au_roc = args[:au_roc] if args.key?(:au_roc) @confidence_metrics = args[:confidence_metrics] if args.key?(:confidence_metrics) @confusion_matrix = args[:confusion_matrix] if args.key?(:confusion_matrix) @log_loss = args[:log_loss] if args.key?(:log_loss) end |