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Showing content from https://cloud.google.com/python/docs/reference/bigquery/3.0.1/google.cloud.bigquery_v2.types.Model below:

Class Model (3.0.1) | Python client library

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

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.

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

ARIMA model fitting metrics.

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

Model evaluation metrics for ARIMA forecasting models.

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

Arima order, can be used for both non-seasonal and seasonal parts.

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

Evaluation metrics for binary classification/classifier models.

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

Evaluation metrics for clustering models.

Type of supported data frequency for time series forecasting models.

Indicates the method to split input data into multiple tables.

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

Data split result. This contains references to the training and evaluation data tables that were used to train the model.

Distance metric used to compute the distance between two points.

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.

This message has oneof_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Indicates the training algorithm to use for matrix factorization models.

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

Global explanations containing the top most important features after training.

Type of supported holiday regions for time series forecasting models.

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

API documentation for bigquery_v2.types.Model.KmeansEnums class.

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

The abstract base class for a message.

Keys and values corresponding to the fields of the message.

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

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

Indicates the learning rate optimization strategy to use.

Loss metric to evaluate model training performance.

Indicates the type of the Model.

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

Evaluation metrics for multi-class classification/classifier models.

OptimizationStrategy(value)

Indicates the optimization strategy used for training.

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

Evaluation metrics used by weighted-ALS models specified by feedback_type=implicit.

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

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

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

API documentation for bigquery_v2.types.Model.SeasonalPeriod class.

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

Information about a single training query run for the model.

Delete the value on the given field.

This is generally equivalent to setting a falsy value.

Return True if the messages are equal, False otherwise.

Return True if the messages are unequal, False otherwise.

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.

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Last updated 2025-08-07 UTC.

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