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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 > AggregateClassificationMetricsMethods
__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.