Class AggregateClassificationMetrics (2.32.0)

Version 2.32.0

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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.

Inheritance

builtins.object > proto.message.Message > AggregateClassificationMetrics

Methods

__delattr__

__delattr__(key)

Delete the value on the given field.

This is generally equivalent to setting a falsy value.

__eq__

__eq__(other)

Return True if the messages are equal, False otherwise.

__ne__

__ne__(other)

Return True if the messages are unequal, False otherwise.

__setattr__

__setattr__(key, value)

Set the value on the given field.

For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.