Class AggregateClassificationMetrics (1.16.0)

Version 1.16.0

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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.

.. attribute:: precision

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.

Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.

The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.

Area Under a ROC Curve. For multiclass this is a macro- averaged metric.

Inheritance

builtins.object > google.protobuf.pyext._message.CMessage > builtins.object > google.protobuf.message.Message > AggregateClassificationMetrics