Class AggregateClassificationMetrics (3.12.0)

Version 3.12.0

• 3.27.0 (latest)
• 3.26.0
• 3.25.0
• 3.24.0
• 3.23.1
• 3.22.0
• 3.21.0
• 3.20.1
• 3.19.0
• 3.18.0
• 3.17.2
• 3.16.0
• 3.15.0
• 3.14.1
• 3.13.0
• 3.12.0
• 3.11.4
• 3.4.0
• 3.3.6
• 3.2.0
• 3.1.0
• 3.0.1
• 2.34.4
• 2.33.0
• 2.32.0
• 2.31.0
• 2.30.1
• 2.29.0
• 2.28.1
• 2.27.1
• 2.26.0
• 2.25.2
• 2.24.1
• 2.23.3
• 2.22.1
• 2.21.0
• 2.20.0
• 2.19.0
• 2.18.0
• 2.17.0
• 2.16.1
• 2.15.0
• 2.14.0
• 2.13.1
• 2.12.0
• 2.11.0
• 2.10.0
• 2.9.0
• 2.8.0
• 2.7.0
• 2.6.2
• 2.5.0
• 2.4.0
• 2.3.1
• 2.2.0
• 2.1.0
• 2.0.0
• 1.28.2
• 1.27.2
• 1.26.1
• 1.25.0
• 1.24.0
• 1.23.1
• 1.22.0
• 1.21.0
• 1.20.0
• 1.19.0
• 1.18.0
• 1.17.0
• 1.16.0

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.

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.