Class EvaluationMetrics (2.23.3) 

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 google.cloud.bigquery_v2.types.Model.RegressionMetrics
Populated for regression models and explicit feedback type matrix factorization models.
binary_classification_metrics google.cloud.bigquery_v2.types.Model.BinaryClassificationMetrics
Populated for binary classification/classifier models.
multi_class_classification_metrics google.cloud.bigquery_v2.types.Model.MultiClassClassificationMetrics
Populated for multi-class classification/classifier models.
clustering_metrics google.cloud.bigquery_v2.types.Model.ClusteringMetrics
Populated for clustering models.
ranking_metrics google.cloud.bigquery_v2.types.Model.RankingMetrics
Populated for implicit feedback type matrix factorization models.
arima_forecasting_metrics google.cloud.bigquery_v2.types.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.