TablesModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)Model metadata specific to AutoML Tables.
Attributes
| Name | Description |
optimization_objective_recall_value |
float
Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". Must be between 0 and 1, inclusive. |
optimization_objective_precision_value |
float
Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". Must be between 0 and 1, inclusive. |
target_column_spec |
google.cloud.automl_v1beta1.types.ColumnSpec
Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it's not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only. |
input_feature_column_specs |
Sequence[google.cloud.automl_v1beta1.types.ColumnSpec]
Column specs of the dataset's primary table's columns, on which the model is trained and which are used as the input for predictions. The target_column as well as, according to dataset's state upon model creation, weight_column, and ml_use_column must never be included here. Only 3 fields are used: - name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table's columns (except the ones listed above) are used for the training and prediction input. - display_name - Output only. - data_type - Output only. |
optimization_objective |
str
Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set. The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used. CLASSIFICATION_BINARY: "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "MINIMIZE_LOG_LOSS" - Minimize log loss. "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve. "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified recall value. "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified precision value. CLASSIFICATION_MULTI_CLASS : "MINIMIZE_LOG_LOSS" (default) - Minimize log loss. REGRESSION: "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE). "MINIMIZE_MAE" - Minimize mean-absolute error (MAE). "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE). |
tables_model_column_info |
Sequence[google.cloud.automl_v1beta1.types.TablesModelColumnInfo]
Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model. |
train_budget_milli_node_hours |
int
Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive. |
train_cost_milli_node_hours |
int
Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget. |
disable_early_stopping |
bool
Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used. |