Module google_api_proto::google::cloud::bigquery::v2::model

Nested message and enum types in Model.

Modules

• arima_forecasting_metrics
  Nested message and enum types in ArimaForecastingMetrics.
• binary_classification_metrics
  Nested message and enum types in BinaryClassificationMetrics.
• boosted_tree_option_enums
  Nested message and enum types in BoostedTreeOptionEnums.
• category_encoding_method
  Nested message and enum types in CategoryEncodingMethod.
• clustering_metrics
  Nested message and enum types in ClusteringMetrics.
• double_hparam_search_space
  Nested message and enum types in DoubleHparamSearchSpace.
• evaluation_metrics
  Nested message and enum types in EvaluationMetrics.
• global_explanation
  Nested message and enum types in GlobalExplanation.
• hparam_tuning_enums
  Nested message and enum types in HparamTuningEnums.
• hparam_tuning_trial
  Nested message and enum types in HparamTuningTrial.
• int_array_hparam_search_space
  Nested message and enum types in IntArrayHparamSearchSpace.
• int_hparam_search_space
  Nested message and enum types in IntHparamSearchSpace.
• kmeans_enums
  Nested message and enum types in KmeansEnums.
• model_registry_option_enums
  Nested message and enum types in ModelRegistryOptionEnums.
• multi_class_classification_metrics
  Nested message and enum types in MultiClassClassificationMetrics.
• pca_solver_option_enums
  Nested message and enum types in PcaSolverOptionEnums.
• seasonal_period
  Nested message and enum types in SeasonalPeriod.
• training_run
  Nested message and enum types in TrainingRun.

Structs

• AggregateClassificationMetrics
  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.
• ArimaFittingMetrics
  ARIMA model fitting metrics.
• ArimaForecastingMetrics
  Model evaluation metrics for ARIMA forecasting models.
• ArimaOrder
  Arima order, can be used for both non-seasonal and seasonal parts.
• BinaryClassificationMetrics
  Evaluation metrics for binary classification/classifier models.
• BoostedTreeOptionEnums
  Enums for XGBoost model type.
• CategoryEncodingMethod
  Encoding methods for categorical features.
• ClusteringMetrics
  Evaluation metrics for clustering models.
• DataSplitResult
  Data split result. This contains references to the training and evaluation data tables that were used to train the model.
• DimensionalityReductionMetrics
  Model evaluation metrics for dimensionality reduction models.
• DoubleHparamSearchSpace
  Search space for a double hyperparameter.
• EvaluationMetrics
  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.
• GlobalExplanation
  Global explanations containing the top most important features after training.
• HparamSearchSpaces
  Hyperparameter search spaces. These should be a subset of training_options.
• HparamTuningEnums
  Enums for hyperparameter tuning.
• HparamTuningTrial
  Training info of a trial in hyperparameter tuning models.
• IntArrayHparamSearchSpace
  Search space for int array.
• IntHparamSearchSpace
  Search space for an int hyperparameter.
• KmeansEnums
  Enums for kmeans model type.
• ModelRegistryOptionEnums
  Model registry options.
• MultiClassClassificationMetrics
  Evaluation metrics for multi-class classification/classifier models.
• PcaSolverOptionEnums
  PCA solver options.
• RankingMetrics
  Evaluation metrics used by weighted-ALS models specified by feedback_type=implicit.
• RegressionMetrics
  Evaluation metrics for regression and explicit feedback type matrix factorization models.
• SeasonalPeriod
  Enums for seasonal period.
• StringHparamSearchSpace
  Search space for string and enum.
• TrainingRun
  Information about a single training query run for the model.

Enums

• ColorSpace
  Enums for color space, used for processing images in Object Table. See more details at https://www.tensorflow.org/io/tutorials/colorspace.
• DataFrequency
  Type of supported data frequency for time series forecasting models.
• DataSplitMethod
  Indicates the method to split input data into multiple tables.
• DistanceType
  Distance metric used to compute the distance between two points.
• FeedbackType
  Indicates the training algorithm to use for matrix factorization models.
• HolidayRegion
  Type of supported holiday regions for time series forecasting models.
• LearnRateStrategy
  Indicates the learning rate optimization strategy to use.
• LossType
  Loss metric to evaluate model training performance.
• ModelType
  Indicates the type of the Model.
• OptimizationStrategy
  Indicates the optimization strategy used for training.