Class BinaryConfusionMatrix (3.11.4)

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

Confusion matrix for binary classification models.

Attributes
Name	Description
`positive_class_threshold`	`google.protobuf.wrappers_pb2.DoubleValue` Threshold value used when computing each of the following metric.
`true_positives`	`google.protobuf.wrappers_pb2.Int64Value` Number of true samples predicted as true.
`false_positives`	`google.protobuf.wrappers_pb2.Int64Value` Number of false samples predicted as true.
`true_negatives`	`google.protobuf.wrappers_pb2.Int64Value` Number of true samples predicted as false.
`false_negatives`	`google.protobuf.wrappers_pb2.Int64Value` Number of false samples predicted as false.
`precision`	`google.protobuf.wrappers_pb2.DoubleValue` The fraction of actual positive predictions that had positive actual labels.
`recall`	`google.protobuf.wrappers_pb2.DoubleValue` The fraction of actual positive labels that were given a positive prediction.
`f1_score`	`google.protobuf.wrappers_pb2.DoubleValue` The equally weighted average of recall and precision.
`accuracy`	`google.protobuf.wrappers_pb2.DoubleValue` The fraction of predictions given the correct label.

Methods

__delattr__(key)

Delete the value on the given field.

This is generally equivalent to setting a falsy value.

__eq__(other)

Return True if the messages are equal, False otherwise.

__ne__(other)

Return True if the messages are unequal, False otherwise.

__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.