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Showing content from https://cloud.google.com/python/docs/reference/bigquery/2.0.0/google.cloud.bigquery_v2.types.Model below:

Class Model (2.0.0) | Python client library

Skip to main content Class Model (2.0.0)

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

Attributes Name Description etag str
Output only. A hash of this resource. model_reference `.gcb_model_reference.ModelReference`
Required. Unique identifier for this model. creation_time int
Output only. The time when this model was created, in millisecs since the epoch. last_modified_time int
Output only. The time when this model was last modified, in millisecs since the epoch. description str
Optional. A user-friendly description of this model. friendly_name str
Optional. A descriptive name for this model. labels Sequence[`.gcb_model.Model.LabelsEntry`]
The labels associated with this model. You can use these to organize and group your models. Label keys and values can be no longer than 63 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter and each label in the list must have a different key. expiration_time int
Optional. The time when this model expires, in milliseconds since the epoch. If not present, the model will persist indefinitely. Expired models will be deleted and their storage reclaimed. The defaultTableExpirationMs property of the encapsulating dataset can be used to set a default expirationTime on newly created models. location str
Output only. The geographic location where the model resides. This value is inherited from the dataset. encryption_configuration `.encryption_config.EncryptionConfiguration`
Custom encryption configuration (e.g., Cloud KMS keys). This shows the encryption configuration of the model data while stored in BigQuery storage. model_type `.gcb_model.Model.ModelType`
Output only. Type of the model resource. training_runs Sequence[`.gcb_model.Model.TrainingRun`]
Output only. Information for all training runs in increasing order of start_time. feature_columns Sequence[`.standard_sql.StandardSqlField`]
Output only. Input feature columns that were used to train this model. label_columns Sequence[`.standard_sql.StandardSqlField`]
Output only. Label columns that were used to train this model. The output of the model will have a `predicted_` prefix to these columns.

Inheritancebuiltins.object > proto.message.Message > Model

Classes AggregateClassificationMetrics

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.

BinaryClassificationMetrics

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

Evaluation metrics for binary classification/classifier models.

ClusteringMetrics

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

Evaluation metrics for clustering models.

DataSplitMethod

Indicates the method to split input data into multiple tables.

DistanceType

Distance metric used to compute the distance between two points.

EvaluationMetrics

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.

LabelsEntry

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

The abstract base class for a message.

Parameters Name Description kwargs dict

Keys and values corresponding to the fields of the message.

mapping Union[dict, `.Message`]

A dictionary or message to be used to determine the values for this message.

ignore_unknown_fields Optional(bool)

If True, do not raise errors for unknown fields. Only applied if mapping is a mapping type or there are keyword parameters.

LearnRateStrategy

Indicates the learning rate optimization strategy to use.

LossType

Loss metric to evaluate model training performance.

ModelType

Indicates the type of the Model.

MultiClassClassificationMetrics

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

Evaluation metrics for multi-class classification/classifier models.

OptimizationStrategy

OptimizationStrategy(value)

Indicates the optimization strategy used for training.

RegressionMetrics

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

Evaluation metrics for regression and explicit feedback type matrix factorization models.

TrainingRun

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

Information about a single training query run for the model.

Methods __delattr__

Delete the value on the given field.

This is generally equivalent to setting a falsy value.

__eq__

Return True if the messages are equal, False otherwise.

__ne__

Return True if the messages are unequal, False otherwise.

__setattr__

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.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-07 UTC.

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