Class ClassificationEvaluationMetrics (2.0.0)

ClassificationEvaluationMetrics(
    mapping=None, *, ignore_unknown_fields=False, **kwargs
)

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

Attributes

Name	Description
`au_prc`	`float` Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.
`au_roc`	`float` Output only. The Area Under Receiver Operating Characteristic curve metric. Micro- averaged for the overall evaluation.
`log_loss`	`float` Output only. The Log Loss metric.
`confidence_metrics_entry`	`Sequence[.classification.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`	`.classification.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.
`annotation_spec_id`	`Sequence[str]` Output only. The annotation spec ids used for this evaluation.

Classes

ConfidenceMetricsEntry

ConfidenceMetricsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Metrics for a single confidence threshold.

ConfusionMatrix

ConfusionMatrix(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Confusion matrix of the model running the classification.