Module gcp_bigquery_client::model
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All the object definitions used by the BigQuery REST API.
Modules§
- 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.
- Input/output argument of a function or a stored procedure.
- Arima coefficients.
- ARIMA model fitting metrics.
- Model evaluation metrics for ARIMA forecasting models.
- Arima model information.
- Arima order, can be used for both non-seasonal and seasonal parts.
- (Auto-)arima fitting result. Wrap everything in ArimaResult for easier refactoring if we want to use model-specific iteration results.
- Model evaluation metrics for a single ARIMA forecasting model.
- Evaluation metrics for binary classification/classifier models.
- Confusion matrix for binary classification models.
- Representative value of a categorical feature.
- Represents the count of a single category within the cluster.
- Message containing the information about one cluster.
- Information about a single cluster for clustering model.
- Evaluation metrics for clustering models.
- Confusion matrix for multi-class classification models.
- Data split result. This contains references to the training and evaluation data tables that were used to train the model.
- Model evaluation metrics for dimensionality reduction models.
- A single entry in the confusion matrix.
- 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.
- Explanation for a single feature.
- Representative value of a single feature within the cluster.
- Global explanations containing the top most important features after training.
- Information about a single iteration of the training run.
- Evaluation metrics for multi-class classification/classifier models.
- Principal component infos, used only for eigen decomposition based models, e.g., PCA. Ordered by explained_variance in the descending order.
- Evaluation metrics used by weighted-ALS models specified by feedback_type=implicit.
- Evaluation metrics for regression and explicit feedback type matrix factorization models.
- A user-defined function or a stored procedure.
- A single row in the confusion matrix.
- Represents access on a subset of rows on the specified table, defined by its filter predicate. Access to the subset of rows is controlled by its IAM policy.
- The type of a variable, e.g., a function argument. Examples: INT64: {type_kind=“INT64”} ARRAY: {type_kind=“ARRAY”, array_element_type=“STRING”} STRUCT>: {type_kind=“STRUCT”, struct_type={fields=[ {name=“x”, type={type_kind=“STRING”}}, {name=“y”, type={type_kind=“ARRAY”, array_element_type=“DATE”}} ]}}
- A field or a column.
- Options used in model training.
- Information about a single training query run for the model.