Class AggregateClassificationMetrics (3.36.0) 

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

Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows.

Attributes
Name Description
precision google.protobuf.wrappers_pb2.DoubleValue
Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier.
recall google.protobuf.wrappers_pb2.DoubleValue
Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
accuracy google.protobuf.wrappers_pb2.DoubleValue
Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
threshold google.protobuf.wrappers_pb2.DoubleValue
Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
f1_score google.protobuf.wrappers_pb2.DoubleValue
The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
log_loss google.protobuf.wrappers_pb2.DoubleValue
Logarithmic Loss. For multiclass this is a macro-averaged metric.
roc_auc google.protobuf.wrappers_pb2.DoubleValue
Area Under a ROC Curve. For multiclass this is a macro-averaged metric.