Module gcp_bigquery_client::model [−][src]
Expand description
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.