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Showing content from https://cloud.google.com/python/docs/reference/bigquery/1.19.0/google.cloud.bigquery_v2.types.Model below:

Class Model (1.19.0) | Python client library

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Protocol buffer.

Required. Unique identifier for this model.

Output only. The time when this model was last modified, in millisecs since the epoch.

[Optional] A descriptive name for this model.

[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.

Output only. Type of the model resource.

Output only. Input feature columns that were used to train this model.

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.

Evaluation metrics for binary classification/classifier models.

.. attribute:: aggregate_classification_metrics

Aggregate classification metrics.

Label representing the positive class.

Evaluation metrics for clustering models.

.. attribute:: davies_bouldin_index

Davies-Bouldin index.

[Beta] Information for all clusters.

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.

.. attribute:: regression_metrics

Populated for regression models and explicit feedback type matrix factorization models.

Populated for multi-class classification/classifier models.

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

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

Evaluation metrics for multi-class classification/classifier models.

.. attribute:: aggregate_classification_metrics

Aggregate classification metrics.

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

.. attribute:: mean_absolute_error

Mean absolute error.

Mean squared log error.

R^2 score.

Information about a single training query run for the model.

.. attribute:: training_options

Options that were used for this training run, includes user specified and default options that were used.

Output of each iteration run, results.size() <= max_iterations.

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Last updated 2025-07-24 UTC.

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