Class ClassificationEvaluationMetrics (0.8.0)

Model evaluation metrics for classification problems.

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

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.

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



Metrics for a single confidence threshold.

Output only. Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold.

Output only. Precision for the given confidence threshold.

Output only. The harmonic mean of recall and precision.

Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

Output only. The harmonic mean of [recall_at1][ utoml.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntr y.recall_at1] and [precision_at1][ ssificationEvaluationMetrics.ConfidenceMetricsEntry.precision _at1].

Output only. The number of model created labels that do not match a ground truth label.

Output only. The number of labels that were not created by the model, but if they would, they would not match a ground truth label.


Confusion matrix of the model running the classification.

Output only. Display name of the annotation specs used in the confusion matrix, as they were at the moment of the evaluation.