Model metadata specific to AutoML Tables.
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][google.cloud.automl.v1beta1 .TablesModelMetadata.target_column_spec] as well as, according to dataset's state upon model creation, [weight_co lumn][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight _column_spec_id], and [ml_use_column][google.cloud.autom l.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id] 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.
Additional optimization objective configuration. Required for
MAXIMIZE_PRECISION_AT_RECALL and
MAXIMIZE_RECALL_AT_PRECISION, otherwise unused.
Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". Must be between 0 and 1, inclusive.
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.
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.