Class EvaluationMetrics (2.3.1)

Version 2.3.1

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

Evaluation metrics of a model. These are either computed on all training data or just the eval data based on whether eval data was used during training. These are not present for imported models.

Attributes

Name	Description
regression_metrics	`.gcb_model.Model.RegressionMetrics` Populated for regression models and explicit feedback type matrix factorization models.
binary_classification_metrics	`.gcb_model.Model.BinaryClassificationMetrics` Populated for binary classification/classifier models.
multi_class_classification_metrics	`.gcb_model.Model.MultiClassClassificationMetrics` Populated for multi-class classification/classifier models.
clustering_metrics	`.gcb_model.Model.ClusteringMetrics` Populated for clustering models.
ranking_metrics	`.gcb_model.Model.RankingMetrics` Populated for implicit feedback type matrix factorization models.
arima_forecasting_metrics	`.gcb_model.Model.ArimaForecastingMetrics` Populated for ARIMA models.

Inheritance

builtins.object > proto.message.Message > EvaluationMetrics

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.