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Model(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Attributes
Name | Description |
etag |
str
Output only. A hash of this resource. |
model_reference |
`.gcb_model_reference.ModelReference`
Required. Unique identifier for this model. |
creation_time |
int
Output only. The time when this model was created, in millisecs since the epoch. |
last_modified_time |
int
Output only. The time when this model was last modified, in millisecs since the epoch. |
description |
str
Optional. A user-friendly description of this model. |
friendly_name |
str
Optional. A descriptive name for this model. |
labels |
Sequence[`.gcb_model.Model.LabelsEntry`]
The labels associated with this model. You can use these to organize and group your models. Label keys and values can be no longer than 63 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter and each label in the list must have a different key. |
expiration_time |
int
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. |
location |
str
Output only. The geographic location where the model resides. This value is inherited from the dataset. |
encryption_configuration |
`.encryption_config.EncryptionConfiguration`
Custom encryption configuration (e.g., Cloud KMS keys). This shows the encryption configuration of the model data while stored in BigQuery storage. |
model_type |
`.gcb_model.Model.ModelType`
Output only. Type of the model resource. |
training_runs |
Sequence[`.gcb_model.Model.TrainingRun`]
Output only. Information for all training runs in increasing order of start_time. |
feature_columns |
Sequence[`.standard_sql.StandardSqlField`]
Output only. Input feature columns that were used to train this model. |
label_columns |
Sequence[`.standard_sql.StandardSqlField`]
Output only. Label columns that were used to train this model. The output of the model will have a `predicted_` prefix to these columns. |
Inheritance
builtins.object > proto.message.Message > ModelClasses
AggregateClassificationMetrics
AggregateClassificationMetrics(
mapping=None, *, ignore_unknown_fields=False, **kwargs
)
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.
BinaryClassificationMetrics
BinaryClassificationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Evaluation metrics for binary classification/classifier models.
ClusteringMetrics
ClusteringMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Evaluation metrics for clustering models.
DataSplitMethod
DataSplitMethod(value)
Indicates the method to split input data into multiple tables.
DistanceType
DistanceType(value)
Distance metric used to compute the distance between two points.
EvaluationMetrics
EvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
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.
LabelsEntry
LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
The abstract base class for a message.
Name | Description |
kwargs |
dict
Keys and values corresponding to the fields of the message. |
mapping |
Union[dict, `.Message`]
A dictionary or message to be used to determine the values for this message. |
ignore_unknown_fields |
Optional(bool)
If True, do not raise errors for unknown fields. Only applied if |
LearnRateStrategy
LearnRateStrategy(value)
Indicates the learning rate optimization strategy to use.
LossType
LossType(value)
Loss metric to evaluate model training performance.
ModelType
ModelType(value)
Indicates the type of the Model.
MultiClassClassificationMetrics
MultiClassClassificationMetrics(
mapping=None, *, ignore_unknown_fields=False, **kwargs
)
Evaluation metrics for multi-class classification/classifier models.
OptimizationStrategy
OptimizationStrategy(value)
Indicates the optimization strategy used for training.
RegressionMetrics
RegressionMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Evaluation metrics for regression and explicit feedback type matrix factorization models.
TrainingRun
TrainingRun(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Information about a single training query run for the model.
Methods
__delattr__
__delattr__(key)
Delete the value on the given field.
This is generally equivalent to setting a falsy value.
__eq__
__eq__(other)
Return True if the messages are equal, False otherwise.
__ne__
__ne__(other)
Return True if the messages are unequal, False otherwise.
__setattr__
__setattr__(key, value)
Set the value on the given field.
For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.