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gcp_bigquery_model

ancestors

Type: UNORDERED_LIST_STRING

best_trial_id

Type: INT64
Provider name: bestTrialId
Description: The best trial_id across all training runs.

creation_time

Type: INT64
Provider name: creationTime
Description: Output only. The time when this model was created, in millisecs since the epoch.

default_trial_id

Type: INT64
Provider name: defaultTrialId
Description: Output only. The default trial_id to use in TVFs when the trial_id is not passed in. For single-objective hyperparameter tuning models, this is the best trial ID. For multi-objective hyperparameter tuning models, this is the smallest trial ID among all Pareto optimal trials.

description

Type: STRING
Provider name: description
Description: Optional. A user-friendly description of this model.

encryption_configuration

Type: STRUCT
Provider name: encryptionConfiguration
Description: Custom encryption configuration (e.g., Cloud KMS keys). This shows the encryption configuration of the model data while stored in BigQuery storage. This field can be used with PatchModel to update encryption key for an already encrypted model.

  • kms_key_name
    Type: STRING
    Provider name: kmsKeyName
    Description: Optional. Describes the Cloud KMS encryption key that will be used to protect destination BigQuery table. The BigQuery Service Account associated with your project requires access to this encryption key.

etag

Type: STRING
Provider name: etag
Description: Output only. A hash of this resource.

expiration_time

Type: INT64
Provider name: expirationTime
Description: 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.

feature_columns

Type: UNORDERED_LIST_STRUCT
Provider name: featureColumns
Description: Output only. Input feature columns that were used to train this model.

  • name
    Type: STRING
    Provider name: name
    Description: Optional. The name of this field. Can be absent for struct fields.
  • type
    Type: STRUCT
    Provider name: type
    Description: Optional. The type of this parameter. Absent if not explicitly specified (e.g., CREATE FUNCTION statement can omit the return type; in this case the output parameter does not have this “type” field).

    • struct_type
      Type: STRUCT
      Provider name: structType
      Description: The fields of this struct, in order, if type_kind = “STRUCT”.

    • type_kind
      Type: STRING
      Provider name: typeKind
      Description: Required. The top level type of this field. Can be any GoogleSQL data type (e.g., “INT64”, “DATE”, “ARRAY”).
      Possible values:

      • TYPE_KIND_UNSPECIFIED - Invalid type.
      • INT64 - Encoded as a string in decimal format.
      • BOOL - Encoded as a boolean ‘false’ or ’true’.
      • FLOAT64 - Encoded as a number, or string ‘NaN
      • STRING - Infinity’ or ‘-Infinity’.
      • BYTES - Encoded as a string value.
      • TIMESTAMP - Encoded as a base64 string per RFC 4648, section 4.
      • DATE - Encoded as an RFC 3339 timestamp with mandatory ‘Z’ time zone string: 1985-04-12T23:20:50.52Z
      • TIME - Encoded as RFC 3339 full-date format string: 1985-04-12
      • DATETIME - Encoded as RFC 3339 partial-time format string: 23:20:50.52
      • INTERVAL - Encoded as RFC 3339 full-date ‘T’ partial-time: 1985-04-12T23:20:50.52
      • GEOGRAPHY - Encoded as fully qualified 3 part: 0-5 15 2:30:45.6
      • NUMERIC - Encoded as WKT
      • BIGNUMERIC - Encoded as a decimal string.
      • JSON - Encoded as a decimal string.
      • ARRAY - Encoded as a string.
      • STRUCT - Encoded as a list with types matching Type.array_type.

friendly_name

Type: STRING
Provider name: friendlyName
Description: Optional. A descriptive name for this model.

hparam_search_spaces

Type: STRUCT
Provider name: hparamSearchSpaces
Description: Output only. All hyperparameter search spaces in this model.

  • activation_fn
    Type: STRUCT
    Provider name: activationFn
    Description: Activation functions of neural network models.
    • candidates
      Type: UNORDERED_LIST_STRING
      Provider name: candidates
      Description: Canididates for the string or enum parameter in lower case.
  • batch_size
    Type: STRUCT
    Provider name: batchSize
    Description: Mini batch sample size.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the int hyperparameter.
      • candidates
        Type: UNORDERED_LIST_INT64
        Provider name: candidates
        Description: Candidates for the int parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the int hyperparameter.
      • max
        Type: INT64
        Provider name: max
        Description: Max value of the int parameter.
      • min
        Type: INT64
        Provider name: min
        Description: Min value of the int parameter.
  • booster_type
    Type: STRUCT
    Provider name: boosterType
    Description: Booster type for boosted tree models.
    • candidates
      Type: UNORDERED_LIST_STRING
      Provider name: candidates
      Description: Canididates for the string or enum parameter in lower case.
  • colsample_bylevel
    Type: STRUCT
    Provider name: colsampleBylevel
    Description: Subsample ratio of columns for each level for boosted tree models.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the double hyperparameter.
      • candidates
        Type: UNORDERED_LIST_DOUBLE
        Provider name: candidates
        Description: Candidates for the double parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the double hyperparameter.
      • max
        Type: DOUBLE
        Provider name: max
        Description: Max value of the double parameter.
      • min
        Type: DOUBLE
        Provider name: min
        Description: Min value of the double parameter.
  • colsample_bynode
    Type: STRUCT
    Provider name: colsampleBynode
    Description: Subsample ratio of columns for each node(split) for boosted tree models.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the double hyperparameter.
      • candidates
        Type: UNORDERED_LIST_DOUBLE
        Provider name: candidates
        Description: Candidates for the double parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the double hyperparameter.
      • max
        Type: DOUBLE
        Provider name: max
        Description: Max value of the double parameter.
      • min
        Type: DOUBLE
        Provider name: min
        Description: Min value of the double parameter.
  • colsample_bytree
    Type: STRUCT
    Provider name: colsampleBytree
    Description: Subsample ratio of columns when constructing each tree for boosted tree models.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the double hyperparameter.
      • candidates
        Type: UNORDERED_LIST_DOUBLE
        Provider name: candidates
        Description: Candidates for the double parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the double hyperparameter.
      • max
        Type: DOUBLE
        Provider name: max
        Description: Max value of the double parameter.
      • min
        Type: DOUBLE
        Provider name: min
        Description: Min value of the double parameter.
  • dart_normalize_type
    Type: STRUCT
    Provider name: dartNormalizeType
    Description: Dart normalization type for boosted tree models.
    • candidates
      Type: UNORDERED_LIST_STRING
      Provider name: candidates
      Description: Canididates for the string or enum parameter in lower case.
  • dropout
    Type: STRUCT
    Provider name: dropout
    Description: Dropout probability for dnn model training and boosted tree models using dart booster.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the double hyperparameter.
      • candidates
        Type: UNORDERED_LIST_DOUBLE
        Provider name: candidates
        Description: Candidates for the double parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the double hyperparameter.
      • max
        Type: DOUBLE
        Provider name: max
        Description: Max value of the double parameter.
      • min
        Type: DOUBLE
        Provider name: min
        Description: Min value of the double parameter.
  • hidden_units
    Type: STRUCT
    Provider name: hiddenUnits
    Description: Hidden units for neural network models.
    • candidates
      Type: UNORDERED_LIST_STRUCT
      Provider name: candidates
      Description: Candidates for the int array parameter.
      • elements
        Type: UNORDERED_LIST_INT64
        Provider name: elements
        Description: Elements in the int array.
  • l1_reg
    Type: STRUCT
    Provider name: l1Reg
    Description: L1 regularization coefficient.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the double hyperparameter.
      • candidates
        Type: UNORDERED_LIST_DOUBLE
        Provider name: candidates
        Description: Candidates for the double parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the double hyperparameter.
      • max
        Type: DOUBLE
        Provider name: max
        Description: Max value of the double parameter.
      • min
        Type: DOUBLE
        Provider name: min
        Description: Min value of the double parameter.
  • l2_reg
    Type: STRUCT
    Provider name: l2Reg
    Description: L2 regularization coefficient.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the double hyperparameter.
      • candidates
        Type: UNORDERED_LIST_DOUBLE
        Provider name: candidates
        Description: Candidates for the double parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the double hyperparameter.
      • max
        Type: DOUBLE
        Provider name: max
        Description: Max value of the double parameter.
      • min
        Type: DOUBLE
        Provider name: min
        Description: Min value of the double parameter.
  • learn_rate
    Type: STRUCT
    Provider name: learnRate
    Description: Learning rate of training jobs.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the double hyperparameter.
      • candidates
        Type: UNORDERED_LIST_DOUBLE
        Provider name: candidates
        Description: Candidates for the double parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the double hyperparameter.
      • max
        Type: DOUBLE
        Provider name: max
        Description: Max value of the double parameter.
      • min
        Type: DOUBLE
        Provider name: min
        Description: Min value of the double parameter.
  • max_tree_depth
    Type: STRUCT
    Provider name: maxTreeDepth
    Description: Maximum depth of a tree for boosted tree models.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the int hyperparameter.
      • candidates
        Type: UNORDERED_LIST_INT64
        Provider name: candidates
        Description: Candidates for the int parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the int hyperparameter.
      • max
        Type: INT64
        Provider name: max
        Description: Max value of the int parameter.
      • min
        Type: INT64
        Provider name: min
        Description: Min value of the int parameter.
  • min_split_loss
    Type: STRUCT
    Provider name: minSplitLoss
    Description: Minimum split loss for boosted tree models.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the double hyperparameter.
      • candidates
        Type: UNORDERED_LIST_DOUBLE
        Provider name: candidates
        Description: Candidates for the double parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the double hyperparameter.
      • max
        Type: DOUBLE
        Provider name: max
        Description: Max value of the double parameter.
      • min
        Type: DOUBLE
        Provider name: min
        Description: Min value of the double parameter.
  • min_tree_child_weight
    Type: STRUCT
    Provider name: minTreeChildWeight
    Description: Minimum sum of instance weight needed in a child for boosted tree models.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the int hyperparameter.
      • candidates
        Type: UNORDERED_LIST_INT64
        Provider name: candidates
        Description: Candidates for the int parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the int hyperparameter.
      • max
        Type: INT64
        Provider name: max
        Description: Max value of the int parameter.
      • min
        Type: INT64
        Provider name: min
        Description: Min value of the int parameter.
  • num_clusters
    Type: STRUCT
    Provider name: numClusters
    Description: Number of clusters for k-means.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the int hyperparameter.
      • candidates
        Type: UNORDERED_LIST_INT64
        Provider name: candidates
        Description: Candidates for the int parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the int hyperparameter.
      • max
        Type: INT64
        Provider name: max
        Description: Max value of the int parameter.
      • min
        Type: INT64
        Provider name: min
        Description: Min value of the int parameter.
  • num_factors
    Type: STRUCT
    Provider name: numFactors
    Description: Number of latent factors to train on.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the int hyperparameter.
      • candidates
        Type: UNORDERED_LIST_INT64
        Provider name: candidates
        Description: Candidates for the int parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the int hyperparameter.
      • max
        Type: INT64
        Provider name: max
        Description: Max value of the int parameter.
      • min
        Type: INT64
        Provider name: min
        Description: Min value of the int parameter.
  • num_parallel_tree
    Type: STRUCT
    Provider name: numParallelTree
    Description: Number of parallel trees for boosted tree models.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the int hyperparameter.
      • candidates
        Type: UNORDERED_LIST_INT64
        Provider name: candidates
        Description: Candidates for the int parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the int hyperparameter.
      • max
        Type: INT64
        Provider name: max
        Description: Max value of the int parameter.
      • min
        Type: INT64
        Provider name: min
        Description: Min value of the int parameter.
  • optimizer
    Type: STRUCT
    Provider name: optimizer
    Description: Optimizer of TF models.
    • candidates
      Type: UNORDERED_LIST_STRING
      Provider name: candidates
      Description: Canididates for the string or enum parameter in lower case.
  • subsample
    Type: STRUCT
    Provider name: subsample
    Description: Subsample the training data to grow tree to prevent overfitting for boosted tree models.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the double hyperparameter.
      • candidates
        Type: UNORDERED_LIST_DOUBLE
        Provider name: candidates
        Description: Candidates for the double parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the double hyperparameter.
      • max
        Type: DOUBLE
        Provider name: max
        Description: Max value of the double parameter.
      • min
        Type: DOUBLE
        Provider name: min
        Description: Min value of the double parameter.
  • tree_method
    Type: STRUCT
    Provider name: treeMethod
    Description: Tree construction algorithm for boosted tree models.
    • candidates
      Type: UNORDERED_LIST_STRING
      Provider name: candidates
      Description: Canididates for the string or enum parameter in lower case.
  • wals_alpha
    Type: STRUCT
    Provider name: walsAlpha
    Description: Hyperparameter for matrix factoration when implicit feedback type is specified.
    • candidates
      Type: STRUCT
      Provider name: candidates
      Description: Candidates of the double hyperparameter.
      • candidates
        Type: UNORDERED_LIST_DOUBLE
        Provider name: candidates
        Description: Candidates for the double parameter in increasing order.
    • range
      Type: STRUCT
      Provider name: range
      Description: Range of the double hyperparameter.
      • max
        Type: DOUBLE
        Provider name: max
        Description: Max value of the double parameter.
      • min
        Type: DOUBLE
        Provider name: min
        Description: Min value of the double parameter.

hparam_trials

Type: UNORDERED_LIST_STRUCT
Provider name: hparamTrials
Description: Output only. Trials of a hyperparameter tuning model sorted by trial_id.

  • end_time_ms
    Type: INT64
    Provider name: endTimeMs
    Description: Ending time of the trial.
  • error_message
    Type: STRING
    Provider name: errorMessage
    Description: Error message for FAILED and INFEASIBLE trial.
  • eval_loss
    Type: DOUBLE
    Provider name: evalLoss
    Description: Loss computed on the eval data at the end of trial.
  • evaluation_metrics
    Type: STRUCT
    Provider name: evaluationMetrics
    Description: Evaluation metrics of this trial calculated on the test data. Empty in Job API.
    • arima_forecasting_metrics
      Type: STRUCT
      Provider name: arimaForecastingMetrics
      Description: Populated for ARIMA models.
      • arima_fitting_metrics
        Type: UNORDERED_LIST_STRUCT
        Provider name: arimaFittingMetrics
        Description: Arima model fitting metrics.
        • aic
          Type: DOUBLE
          Provider name: aic
          Description: AIC.
        • log_likelihood
          Type: DOUBLE
          Provider name: logLikelihood
          Description: Log-likelihood.
        • variance
          Type: DOUBLE
          Provider name: variance
          Description: Variance.
      • arima_single_model_forecasting_metrics
        Type: UNORDERED_LIST_STRUCT
        Provider name: arimaSingleModelForecastingMetrics
        Description: Repeated as there can be many metric sets (one for each model) in auto-arima and the large-scale case.
        • arima_fitting_metrics
          Type: STRUCT
          Provider name: arimaFittingMetrics
          Description: Arima fitting metrics.
          • aic
            Type: DOUBLE
            Provider name: aic
            Description: AIC.
          • log_likelihood
            Type: DOUBLE
            Provider name: logLikelihood
            Description: Log-likelihood.
          • variance
            Type: DOUBLE
            Provider name: variance
            Description: Variance.
        • has_drift
          Type: BOOLEAN
          Provider name: hasDrift
          Description: Is arima model fitted with drift or not. It is always false when d is not 1.
        • has_holiday_effect
          Type: BOOLEAN
          Provider name: hasHolidayEffect
          Description: If true, holiday_effect is a part of time series decomposition result.
        • has_spikes_and_dips
          Type: BOOLEAN
          Provider name: hasSpikesAndDips
          Description: If true, spikes_and_dips is a part of time series decomposition result.
        • has_step_changes
          Type: BOOLEAN
          Provider name: hasStepChanges
          Description: If true, step_changes is a part of time series decomposition result.
        • non_seasonal_order
          Type: STRUCT
          Provider name: nonSeasonalOrder
          Description: Non-seasonal order.
          • d
            Type: INT64
            Provider name: d
            Description: Order of the differencing part.
          • p
            Type: INT64
            Provider name: p
            Description: Order of the autoregressive part.
          • q
            Type: INT64
            Provider name: q
            Description: Order of the moving-average part.
        • seasonal_periods
          Type: UNORDERED_LIST_STRING
          Provider name: seasonalPeriods
          Description: Seasonal periods. Repeated because multiple periods are supported for one time series.
        • time_series_id
          Type: STRING
          Provider name: timeSeriesId
          Description: The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
        • time_series_ids
          Type: UNORDERED_LIST_STRING
          Provider name: timeSeriesIds
          Description: The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
      • has_drift
        Type: UNORDERED_LIST_BOOLEAN
        Provider name: hasDrift
        Description: Whether Arima model fitted with drift or not. It is always false when d is not 1.
      • non_seasonal_order
        Type: UNORDERED_LIST_STRUCT
        Provider name: nonSeasonalOrder
        Description: Non-seasonal order.
        • d
          Type: INT64
          Provider name: d
          Description: Order of the differencing part.
        • p
          Type: INT64
          Provider name: p
          Description: Order of the autoregressive part.
        • q
          Type: INT64
          Provider name: q
          Description: Order of the moving-average part.
      • seasonal_periods
        Type: UNORDERED_LIST_STRING
        Provider name: seasonalPeriods
        Description: Seasonal periods. Repeated because multiple periods are supported for one time series.
      • time_series_id
        Type: UNORDERED_LIST_STRING
        Provider name: timeSeriesId
        Description: Id to differentiate different time series for the large-scale case.
    • binary_classification_metrics
      Type: STRUCT
      Provider name: binaryClassificationMetrics
      Description: Populated for binary classification/classifier models.
      • aggregate_classification_metrics
        Type: STRUCT
        Provider name: aggregateClassificationMetrics
        Description: Aggregate classification metrics.
        • accuracy
          Type: DOUBLE
          Provider name: accuracy
          Description: Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
        • f1_score
          Type: DOUBLE
          Provider name: f1Score
          Description: The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
        • log_loss
          Type: DOUBLE
          Provider name: logLoss
          Description: Logarithmic Loss. For multiclass this is a macro-averaged metric.
        • precision
          Type: DOUBLE
          Provider name: precision
          Description: Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier.
        • recall
          Type: DOUBLE
          Provider name: recall
          Description: Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
        • roc_auc
          Type: DOUBLE
          Provider name: rocAuc
          Description: Area Under a ROC Curve. For multiclass this is a macro-averaged metric.
        • threshold
          Type: DOUBLE
          Provider name: threshold
          Description: Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
      • binary_confusion_matrix_list
        Type: UNORDERED_LIST_STRUCT
        Provider name: binaryConfusionMatrixList
        Description: Binary confusion matrix at multiple thresholds.
        • accuracy
          Type: DOUBLE
          Provider name: accuracy
          Description: The fraction of predictions given the correct label.
        • f1_score
          Type: DOUBLE
          Provider name: f1Score
          Description: The equally weighted average of recall and precision.
        • false_negatives
          Type: INT64
          Provider name: falseNegatives
          Description: Number of false samples predicted as false.
        • false_positives
          Type: INT64
          Provider name: falsePositives
          Description: Number of false samples predicted as true.
        • positive_class_threshold
          Type: DOUBLE
          Provider name: positiveClassThreshold
          Description: Threshold value used when computing each of the following metric.
        • precision
          Type: DOUBLE
          Provider name: precision
          Description: The fraction of actual positive predictions that had positive actual labels.
        • recall
          Type: DOUBLE
          Provider name: recall
          Description: The fraction of actual positive labels that were given a positive prediction.
        • true_negatives
          Type: INT64
          Provider name: trueNegatives
          Description: Number of true samples predicted as false.
        • true_positives
          Type: INT64
          Provider name: truePositives
          Description: Number of true samples predicted as true.
      • negative_label
        Type: STRING
        Provider name: negativeLabel
        Description: Label representing the negative class.
      • positive_label
        Type: STRING
        Provider name: positiveLabel
        Description: Label representing the positive class.
    • clustering_metrics
      Type: STRUCT
      Provider name: clusteringMetrics
      Description: Populated for clustering models.
      • clusters
        Type: UNORDERED_LIST_STRUCT
        Provider name: clusters
        Description: Information for all clusters.
        • centroid_id
          Type: INT64
          Provider name: centroidId
          Description: Centroid id.
        • count
          Type: INT64
          Provider name: count
          Description: Count of training data rows that were assigned to this cluster.
        • feature_values
          Type: UNORDERED_LIST_STRUCT
          Provider name: featureValues
          Description: Values of highly variant features for this cluster.
          • categorical_value
            Type: STRUCT
            Provider name: categoricalValue
            Description: The categorical feature value.
            • category_counts
              Type: UNORDERED_LIST_STRUCT
              Provider name: categoryCounts
              Description: Counts of all categories for the categorical feature. If there are more than ten categories, we return top ten (by count) and return one more CategoryCount with category “OTHER” and count as aggregate counts of remaining categories.
              • category
                Type: STRING
                Provider name: category
                Description: The name of category.
              • count
                Type: INT64
                Provider name: count
                Description: The count of training samples matching the category within the cluster.
          • feature_column
            Type: STRING
            Provider name: featureColumn
            Description: The feature column name.
          • numerical_value
            Type: DOUBLE
            Provider name: numericalValue
            Description: The numerical feature value. This is the centroid value for this feature.
      • davies_bouldin_index
        Type: DOUBLE
        Provider name: daviesBouldinIndex
        Description: Davies-Bouldin index.
      • mean_squared_distance
        Type: DOUBLE
        Provider name: meanSquaredDistance
        Description: Mean of squared distances between each sample to its cluster centroid.
    • dimensionality_reduction_metrics
      Type: STRUCT
      Provider name: dimensionalityReductionMetrics
      Description: Evaluation metrics when the model is a dimensionality reduction model, which currently includes PCA.
      • total_explained_variance_ratio
        Type: DOUBLE
        Provider name: totalExplainedVarianceRatio
        Description: Total percentage of variance explained by the selected principal components.
    • multi_class_classification_metrics
      Type: STRUCT
      Provider name: multiClassClassificationMetrics
      Description: Populated for multi-class classification/classifier models.
      • aggregate_classification_metrics
        Type: STRUCT
        Provider name: aggregateClassificationMetrics
        Description: Aggregate classification metrics.
        • accuracy
          Type: DOUBLE
          Provider name: accuracy
          Description: Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
        • f1_score
          Type: DOUBLE
          Provider name: f1Score
          Description: The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
        • log_loss
          Type: DOUBLE
          Provider name: logLoss
          Description: Logarithmic Loss. For multiclass this is a macro-averaged metric.
        • precision
          Type: DOUBLE
          Provider name: precision
          Description: Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier.
        • recall
          Type: DOUBLE
          Provider name: recall
          Description: Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
        • roc_auc
          Type: DOUBLE
          Provider name: rocAuc
          Description: Area Under a ROC Curve. For multiclass this is a macro-averaged metric.
        • threshold
          Type: DOUBLE
          Provider name: threshold
          Description: Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
      • confusion_matrix_list
        Type: UNORDERED_LIST_STRUCT
        Provider name: confusionMatrixList
        Description: Confusion matrix at different thresholds.
        • confidence_threshold
          Type: DOUBLE
          Provider name: confidenceThreshold
          Description: Confidence threshold used when computing the entries of the confusion matrix.
        • rows
          Type: UNORDERED_LIST_STRUCT
          Provider name: rows
          Description: One row per actual label.
          • actual_label
            Type: STRING
            Provider name: actualLabel
            Description: The original label of this row.
          • entries
            Type: UNORDERED_LIST_STRUCT
            Provider name: entries
            Description: Info describing predicted label distribution.
            • item_count
              Type: INT64
              Provider name: itemCount
              Description: Number of items being predicted as this label.
            • predicted_label
              Type: STRING
              Provider name: predictedLabel
              Description: The predicted label. For confidence_threshold > 0, we will also add an entry indicating the number of items under the confidence threshold.
    • ranking_metrics
      Type: STRUCT
      Provider name: rankingMetrics
      Description: Populated for implicit feedback type matrix factorization models.
      • average_rank
        Type: DOUBLE
        Provider name: averageRank
        Description: Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank.
      • mean_average_precision
        Type: DOUBLE
        Provider name: meanAveragePrecision
        Description: Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users.
      • mean_squared_error
        Type: DOUBLE
        Provider name: meanSquaredError
        Description: Similar to the mean squared error computed in regression and explicit recommendation models except instead of computing the rating directly, the output from evaluate is computed against a preference which is 1 or 0 depending on if the rating exists or not.
      • normalized_discounted_cumulative_gain
        Type: DOUBLE
        Provider name: normalizedDiscountedCumulativeGain
        Description: A metric to determine the goodness of a ranking calculated from the predicted confidence by comparing it to an ideal rank measured by the original ratings.
    • regression_metrics
      Type: STRUCT
      Provider name: regressionMetrics
      Description: Populated for regression models and explicit feedback type matrix factorization models.
      • mean_absolute_error
        Type: DOUBLE
        Provider name: meanAbsoluteError
        Description: Mean absolute error.
      • mean_squared_error
        Type: DOUBLE
        Provider name: meanSquaredError
        Description: Mean squared error.
      • mean_squared_log_error
        Type: DOUBLE
        Provider name: meanSquaredLogError
        Description: Mean squared log error.
      • median_absolute_error
        Type: DOUBLE
        Provider name: medianAbsoluteError
        Description: Median absolute error.
      • r_squared
        Type: DOUBLE
        Provider name: rSquared
        Description: R^2 score. This corresponds to r2_score in ML.EVALUATE.
  • gcp_status
    Type: STRING
    Provider name: status
    Description: The status of the trial.
    Possible values:
    • TRIAL_STATUS_UNSPECIFIED
    • NOT_STARTED - Scheduled but not started.
    • RUNNING - Running state.
    • SUCCEEDED - The trial succeeded.
    • FAILED - The trial failed.
    • INFEASIBLE - The trial is infeasible due to the invalid params.
    • STOPPED_EARLY - Trial stopped early because it’s not promising.
  • hparam_tuning_evaluation_metrics
    Type: STRUCT
    Provider name: hparamTuningEvaluationMetrics
    Description: Hyperparameter tuning evaluation metrics of this trial calculated on the eval data. Unlike evaluation_metrics, only the fields corresponding to the hparam_tuning_objectives are set.
    • arima_forecasting_metrics
      Type: STRUCT
      Provider name: arimaForecastingMetrics
      Description: Populated for ARIMA models.
      • arima_fitting_metrics
        Type: UNORDERED_LIST_STRUCT
        Provider name: arimaFittingMetrics
        Description: Arima model fitting metrics.
        • aic
          Type: DOUBLE
          Provider name: aic
          Description: AIC.
        • log_likelihood
          Type: DOUBLE
          Provider name: logLikelihood
          Description: Log-likelihood.
        • variance
          Type: DOUBLE
          Provider name: variance
          Description: Variance.
      • arima_single_model_forecasting_metrics
        Type: UNORDERED_LIST_STRUCT
        Provider name: arimaSingleModelForecastingMetrics
        Description: Repeated as there can be many metric sets (one for each model) in auto-arima and the large-scale case.
        • arima_fitting_metrics
          Type: STRUCT
          Provider name: arimaFittingMetrics
          Description: Arima fitting metrics.
          • aic
            Type: DOUBLE
            Provider name: aic
            Description: AIC.
          • log_likelihood
            Type: DOUBLE
            Provider name: logLikelihood
            Description: Log-likelihood.
          • variance
            Type: DOUBLE
            Provider name: variance
            Description: Variance.
        • has_drift
          Type: BOOLEAN
          Provider name: hasDrift
          Description: Is arima model fitted with drift or not. It is always false when d is not 1.
        • has_holiday_effect
          Type: BOOLEAN
          Provider name: hasHolidayEffect
          Description: If true, holiday_effect is a part of time series decomposition result.
        • has_spikes_and_dips
          Type: BOOLEAN
          Provider name: hasSpikesAndDips
          Description: If true, spikes_and_dips is a part of time series decomposition result.
        • has_step_changes
          Type: BOOLEAN
          Provider name: hasStepChanges
          Description: If true, step_changes is a part of time series decomposition result.
        • non_seasonal_order
          Type: STRUCT
          Provider name: nonSeasonalOrder
          Description: Non-seasonal order.
          • d
            Type: INT64
            Provider name: d
            Description: Order of the differencing part.
          • p
            Type: INT64
            Provider name: p
            Description: Order of the autoregressive part.
          • q
            Type: INT64
            Provider name: q
            Description: Order of the moving-average part.
        • seasonal_periods
          Type: UNORDERED_LIST_STRING
          Provider name: seasonalPeriods
          Description: Seasonal periods. Repeated because multiple periods are supported for one time series.
        • time_series_id
          Type: STRING
          Provider name: timeSeriesId
          Description: The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
        • time_series_ids
          Type: UNORDERED_LIST_STRING
          Provider name: timeSeriesIds
          Description: The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
      • has_drift
        Type: UNORDERED_LIST_BOOLEAN
        Provider name: hasDrift
        Description: Whether Arima model fitted with drift or not. It is always false when d is not 1.
      • non_seasonal_order
        Type: UNORDERED_LIST_STRUCT
        Provider name: nonSeasonalOrder
        Description: Non-seasonal order.
        • d
          Type: INT64
          Provider name: d
          Description: Order of the differencing part.
        • p
          Type: INT64
          Provider name: p
          Description: Order of the autoregressive part.
        • q
          Type: INT64
          Provider name: q
          Description: Order of the moving-average part.
      • seasonal_periods
        Type: UNORDERED_LIST_STRING
        Provider name: seasonalPeriods
        Description: Seasonal periods. Repeated because multiple periods are supported for one time series.
      • time_series_id
        Type: UNORDERED_LIST_STRING
        Provider name: timeSeriesId
        Description: Id to differentiate different time series for the large-scale case.
    • binary_classification_metrics
      Type: STRUCT
      Provider name: binaryClassificationMetrics
      Description: Populated for binary classification/classifier models.
      • aggregate_classification_metrics
        Type: STRUCT
        Provider name: aggregateClassificationMetrics
        Description: Aggregate classification metrics.
        • accuracy
          Type: DOUBLE
          Provider name: accuracy
          Description: Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
        • f1_score
          Type: DOUBLE
          Provider name: f1Score
          Description: The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
        • log_loss
          Type: DOUBLE
          Provider name: logLoss
          Description: Logarithmic Loss. For multiclass this is a macro-averaged metric.
        • precision
          Type: DOUBLE
          Provider name: precision
          Description: Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier.
        • recall
          Type: DOUBLE
          Provider name: recall
          Description: Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
        • roc_auc
          Type: DOUBLE
          Provider name: rocAuc
          Description: Area Under a ROC Curve. For multiclass this is a macro-averaged metric.
        • threshold
          Type: DOUBLE
          Provider name: threshold
          Description: Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
      • binary_confusion_matrix_list
        Type: UNORDERED_LIST_STRUCT
        Provider name: binaryConfusionMatrixList
        Description: Binary confusion matrix at multiple thresholds.
        • accuracy
          Type: DOUBLE
          Provider name: accuracy
          Description: The fraction of predictions given the correct label.
        • f1_score
          Type: DOUBLE
          Provider name: f1Score
          Description: The equally weighted average of recall and precision.
        • false_negatives
          Type: INT64
          Provider name: falseNegatives
          Description: Number of false samples predicted as false.
        • false_positives
          Type: INT64
          Provider name: falsePositives
          Description: Number of false samples predicted as true.
        • positive_class_threshold
          Type: DOUBLE
          Provider name: positiveClassThreshold
          Description: Threshold value used when computing each of the following metric.
        • precision
          Type: DOUBLE
          Provider name: precision
          Description: The fraction of actual positive predictions that had positive actual labels.
        • recall
          Type: DOUBLE
          Provider name: recall
          Description: The fraction of actual positive labels that were given a positive prediction.
        • true_negatives
          Type: INT64
          Provider name: trueNegatives
          Description: Number of true samples predicted as false.
        • true_positives
          Type: INT64
          Provider name: truePositives
          Description: Number of true samples predicted as true.
      • negative_label
        Type: STRING
        Provider name: negativeLabel
        Description: Label representing the negative class.
      • positive_label
        Type: STRING
        Provider name: positiveLabel
        Description: Label representing the positive class.
    • clustering_metrics
      Type: STRUCT
      Provider name: clusteringMetrics
      Description: Populated for clustering models.
      • clusters
        Type: UNORDERED_LIST_STRUCT
        Provider name: clusters
        Description: Information for all clusters.
        • centroid_id
          Type: INT64
          Provider name: centroidId
          Description: Centroid id.
        • count
          Type: INT64
          Provider name: count
          Description: Count of training data rows that were assigned to this cluster.
        • feature_values
          Type: UNORDERED_LIST_STRUCT
          Provider name: featureValues
          Description: Values of highly variant features for this cluster.
          • categorical_value
            Type: STRUCT
            Provider name: categoricalValue
            Description: The categorical feature value.
            • category_counts
              Type: UNORDERED_LIST_STRUCT
              Provider name: categoryCounts
              Description: Counts of all categories for the categorical feature. If there are more than ten categories, we return top ten (by count) and return one more CategoryCount with category “OTHER” and count as aggregate counts of remaining categories.
              • category
                Type: STRING
                Provider name: category
                Description: The name of category.
              • count
                Type: INT64
                Provider name: count
                Description: The count of training samples matching the category within the cluster.
          • feature_column
            Type: STRING
            Provider name: featureColumn
            Description: The feature column name.
          • numerical_value
            Type: DOUBLE
            Provider name: numericalValue
            Description: The numerical feature value. This is the centroid value for this feature.
      • davies_bouldin_index
        Type: DOUBLE
        Provider name: daviesBouldinIndex
        Description: Davies-Bouldin index.
      • mean_squared_distance
        Type: DOUBLE
        Provider name: meanSquaredDistance
        Description: Mean of squared distances between each sample to its cluster centroid.
    • dimensionality_reduction_metrics
      Type: STRUCT
      Provider name: dimensionalityReductionMetrics
      Description: Evaluation metrics when the model is a dimensionality reduction model, which currently includes PCA.
      • total_explained_variance_ratio
        Type: DOUBLE
        Provider name: totalExplainedVarianceRatio
        Description: Total percentage of variance explained by the selected principal components.
    • multi_class_classification_metrics
      Type: STRUCT
      Provider name: multiClassClassificationMetrics
      Description: Populated for multi-class classification/classifier models.
      • aggregate_classification_metrics
        Type: STRUCT
        Provider name: aggregateClassificationMetrics
        Description: Aggregate classification metrics.
        • accuracy
          Type: DOUBLE
          Provider name: accuracy
          Description: Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
        • f1_score
          Type: DOUBLE
          Provider name: f1Score
          Description: The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
        • log_loss
          Type: DOUBLE
          Provider name: logLoss
          Description: Logarithmic Loss. For multiclass this is a macro-averaged metric.
        • precision
          Type: DOUBLE
          Provider name: precision
          Description: Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier.
        • recall
          Type: DOUBLE
          Provider name: recall
          Description: Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
        • roc_auc
          Type: DOUBLE
          Provider name: rocAuc
          Description: Area Under a ROC Curve. For multiclass this is a macro-averaged metric.
        • threshold
          Type: DOUBLE
          Provider name: threshold
          Description: Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
      • confusion_matrix_list
        Type: UNORDERED_LIST_STRUCT
        Provider name: confusionMatrixList
        Description: Confusion matrix at different thresholds.
        • confidence_threshold
          Type: DOUBLE
          Provider name: confidenceThreshold
          Description: Confidence threshold used when computing the entries of the confusion matrix.
        • rows
          Type: UNORDERED_LIST_STRUCT
          Provider name: rows
          Description: One row per actual label.
          • actual_label
            Type: STRING
            Provider name: actualLabel
            Description: The original label of this row.
          • entries
            Type: UNORDERED_LIST_STRUCT
            Provider name: entries
            Description: Info describing predicted label distribution.
            • item_count
              Type: INT64
              Provider name: itemCount
              Description: Number of items being predicted as this label.
            • predicted_label
              Type: STRING
              Provider name: predictedLabel
              Description: The predicted label. For confidence_threshold > 0, we will also add an entry indicating the number of items under the confidence threshold.
    • ranking_metrics
      Type: STRUCT
      Provider name: rankingMetrics
      Description: Populated for implicit feedback type matrix factorization models.
      • average_rank
        Type: DOUBLE
        Provider name: averageRank
        Description: Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank.
      • mean_average_precision
        Type: DOUBLE
        Provider name: meanAveragePrecision
        Description: Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users.
      • mean_squared_error
        Type: DOUBLE
        Provider name: meanSquaredError
        Description: Similar to the mean squared error computed in regression and explicit recommendation models except instead of computing the rating directly, the output from evaluate is computed against a preference which is 1 or 0 depending on if the rating exists or not.
      • normalized_discounted_cumulative_gain
        Type: DOUBLE
        Provider name: normalizedDiscountedCumulativeGain
        Description: A metric to determine the goodness of a ranking calculated from the predicted confidence by comparing it to an ideal rank measured by the original ratings.
    • regression_metrics
      Type: STRUCT
      Provider name: regressionMetrics
      Description: Populated for regression models and explicit feedback type matrix factorization models.
      • mean_absolute_error
        Type: DOUBLE
        Provider name: meanAbsoluteError
        Description: Mean absolute error.
      • mean_squared_error
        Type: DOUBLE
        Provider name: meanSquaredError
        Description: Mean squared error.
      • mean_squared_log_error
        Type: DOUBLE
        Provider name: meanSquaredLogError
        Description: Mean squared log error.
      • median_absolute_error
        Type: DOUBLE
        Provider name: medianAbsoluteError
        Description: Median absolute error.
      • r_squared
        Type: DOUBLE
        Provider name: rSquared
        Description: R^2 score. This corresponds to r2_score in ML.EVALUATE.
  • hparams
    Type: STRUCT
    Provider name: hparams
    Description: The hyperprameters selected for this trial.
    • adjust_step_changes
      Type: BOOLEAN
      Provider name: adjustStepChanges
      Description: If true, detect step changes and make data adjustment in the input time series.
    • approx_global_feature_contrib
      Type: BOOLEAN
      Provider name: approxGlobalFeatureContrib
      Description: Whether to use approximate feature contribution method in XGBoost model explanation for global explain.
    • auto_arima
      Type: BOOLEAN
      Provider name: autoArima
      Description: Whether to enable auto ARIMA or not.
    • auto_arima_max_order
      Type: INT64
      Provider name: autoArimaMaxOrder
      Description: The max value of the sum of non-seasonal p and q.
    • auto_arima_min_order
      Type: INT64
      Provider name: autoArimaMinOrder
      Description: The min value of the sum of non-seasonal p and q.
    • batch_size
      Type: INT64
      Provider name: batchSize
      Description: Batch size for dnn models.
    • booster_type
      Type: STRING
      Provider name: boosterType
      Description: Booster type for boosted tree models.
      Possible values:
      • BOOSTER_TYPE_UNSPECIFIED - Unspecified booster type.
      • GBTREE - Gbtree booster.
      • DART - Dart booster.
    • calculate_p_values
      Type: BOOLEAN
      Provider name: calculatePValues
      Description: Whether or not p-value test should be computed for this model. Only available for linear and logistic regression models.
    • clean_spikes_and_dips
      Type: BOOLEAN
      Provider name: cleanSpikesAndDips
      Description: If true, clean spikes and dips in the input time series.
    • color_space
      Type: STRING
      Provider name: colorSpace
      Description: Enums for color space, used for processing images in Object Table. See more details at https://www.tensorflow.org/io/tutorials/colorspace.
      Possible values:
      • COLOR_SPACE_UNSPECIFIED - Unspecified color space
      • RGB - RGB
      • HSV - HSV
      • YIQ - YIQ
      • YUV - YUV
      • GRAYSCALE - GRAYSCALE
    • colsample_bylevel
      Type: DOUBLE
      Provider name: colsampleBylevel
      Description: Subsample ratio of columns for each level for boosted tree models.
    • colsample_bynode
      Type: DOUBLE
      Provider name: colsampleBynode
      Description: Subsample ratio of columns for each node(split) for boosted tree models.
    • colsample_bytree
      Type: DOUBLE
      Provider name: colsampleBytree
      Description: Subsample ratio of columns when constructing each tree for boosted tree models.
    • dart_normalize_type
      Type: STRING
      Provider name: dartNormalizeType
      Description: Type of normalization algorithm for boosted tree models using dart booster.
      Possible values:
      • DART_NORMALIZE_TYPE_UNSPECIFIED - Unspecified dart normalize type.
      • TREE - New trees have the same weight of each of dropped trees.
      • FOREST - New trees have the same weight of sum of dropped trees.
    • data_frequency
      Type: STRING
      Provider name: dataFrequency
      Description: The data frequency of a time series.
      Possible values:
      • DATA_FREQUENCY_UNSPECIFIED
      • AUTO_FREQUENCY - Automatically inferred from timestamps.
      • YEARLY - Yearly data.
      • QUARTERLY - Quarterly data.
      • MONTHLY - Monthly data.
      • WEEKLY - Weekly data.
      • DAILY - Daily data.
      • HOURLY - Hourly data.
      • PER_MINUTE - Per-minute data.
    • data_split_column
      Type: STRING
      Provider name: dataSplitColumn
      Description: The column to split data with. This column won’t be used as a feature. 1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data. 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
    • data_split_eval_fraction
      Type: DOUBLE
      Provider name: dataSplitEvalFraction
      Description: The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.
    • data_split_method
      Type: STRING
      Provider name: dataSplitMethod
      Description: The data split type for training and evaluation, e.g. RANDOM.
      Possible values:
      • DATA_SPLIT_METHOD_UNSPECIFIED
      • RANDOM - Splits data randomly.
      • CUSTOM - Splits data with the user provided tags.
      • SEQUENTIAL - Splits data sequentially.
      • NO_SPLIT - Data split will be skipped.
      • AUTO_SPLIT - Splits data automatically: Uses NO_SPLIT if the data size is small. Otherwise uses RANDOM.
    • decompose_time_series
      Type: BOOLEAN
      Provider name: decomposeTimeSeries
      Description: If true, perform decompose time series and save the results.
    • distance_type
      Type: STRING
      Provider name: distanceType
      Description: Distance type for clustering models.
      Possible values:
      • DISTANCE_TYPE_UNSPECIFIED
      • EUCLIDEAN - Eculidean distance.
      • COSINE - Cosine distance.
    • dropout
      Type: DOUBLE
      Provider name: dropout
      Description: Dropout probability for dnn models.
    • early_stop
      Type: BOOLEAN
      Provider name: earlyStop
      Description: Whether to stop early when the loss doesn’t improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms.
    • enable_global_explain
      Type: BOOLEAN
      Provider name: enableGlobalExplain
      Description: If true, enable global explanation during training.
    • feedback_type
      Type: STRING
      Provider name: feedbackType
      Description: Feedback type that specifies which algorithm to run for matrix factorization.
      Possible values:
      • FEEDBACK_TYPE_UNSPECIFIED
      • IMPLICIT - Use weighted-als for implicit feedback problems.
      • EXPLICIT - Use nonweighted-als for explicit feedback problems.
    • hidden_units
      Type: UNORDERED_LIST_INT64
      Provider name: hiddenUnits
      Description: Hidden units for dnn models.
    • holiday_region
      Type: STRING
      Provider name: holidayRegion
      Description: The geographical region based on which the holidays are considered in time series modeling. If a valid value is specified, then holiday effects modeling is enabled.
      Possible values:
      • HOLIDAY_REGION_UNSPECIFIED - Holiday region unspecified.
      • GLOBAL - Global.
      • NA - North America.
      • JAPAC - Japan and Asia Pacific: Korea, Greater China, India, Australia, and New Zealand.
      • EMEA - Europe, the Middle East and Africa.
      • LAC - Latin America and the Caribbean.
      • AE - United Arab Emirates
      • AR - Argentina
      • AT - Austria
      • AU - Australia
      • BE - Belgium
      • BR - Brazil
      • CA - Canada
      • CH - Switzerland
      • CL - Chile
      • CN - China
      • CO - Colombia
      • CS - Czechoslovakia
      • CZ - Czech Republic
      • DE - Germany
      • DK - Denmark
      • DZ - Algeria
      • EC - Ecuador
      • EE - Estonia
      • EG - Egypt
      • ES - Spain
      • FI - Finland
      • FR - France
      • GB - Great Britain (United Kingdom)
      • GR - Greece
      • HK - Hong Kong
      • HU - Hungary
      • ID - Indonesia
      • IE - Ireland
      • IL - Israel
      • IN - India
      • IR - Iran
      • IT - Italy
      • JP - Japan
      • KR - Korea (South)
      • LV - Latvia
      • MA - Morocco
      • MX - Mexico
      • MY - Malaysia
      • NG - Nigeria
      • NL - Netherlands
      • NO - Norway
      • NZ - New Zealand
      • PE - Peru
      • PH - Philippines
      • PK - Pakistan
      • PL - Poland
      • PT - Portugal
      • RO - Romania
      • RS - Serbia
      • RU - Russian Federation
      • SA - Saudi Arabia
      • SE - Sweden
      • SG - Singapore
      • SI - Slovenia
      • SK - Slovakia
      • TH - Thailand
      • TR - Turkey
      • TW - Taiwan
      • UA - Ukraine
      • US - United States
      • VE - Venezuela
      • VN - Viet Nam
      • ZA - South Africa
    • horizon
      Type: INT64
      Provider name: horizon
      Description: The number of periods ahead that need to be forecasted.
    • hparam_tuning_objectives
      Type: UNORDERED_LIST_STRING
      Provider name: hparamTuningObjectives
      Description: The target evaluation metrics to optimize the hyperparameters for.
    • include_drift
      Type: BOOLEAN
      Provider name: includeDrift
      Description: Include drift when fitting an ARIMA model.
    • initial_learn_rate
      Type: DOUBLE
      Provider name: initialLearnRate
      Description: Specifies the initial learning rate for the line search learn rate strategy.
    • input_label_columns
      Type: UNORDERED_LIST_STRING
      Provider name: inputLabelColumns
      Description: Name of input label columns in training data.
    • instance_weight_column
      Type: STRING
      Provider name: instanceWeightColumn
      Description: Name of the instance weight column for training data. This column isn’t be used as a feature.
    • integrated_gradients_num_steps
      Type: INT64
      Provider name: integratedGradientsNumSteps
      Description: Number of integral steps for the integrated gradients explain method.
    • item_column
      Type: STRING
      Provider name: itemColumn
      Description: Item column specified for matrix factorization models.
    • kmeans_initialization_column
      Type: STRING
      Provider name: kmeansInitializationColumn
      Description: The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
    • kmeans_initialization_method
      Type: STRING
      Provider name: kmeansInitializationMethod
      Description: The method used to initialize the centroids for kmeans algorithm.
      Possible values:
      • KMEANS_INITIALIZATION_METHOD_UNSPECIFIED - Unspecified initialization method.
      • RANDOM - Initializes the centroids randomly.
      • CUSTOM - Initializes the centroids using data specified in kmeans_initialization_column.
      • KMEANS_PLUS_PLUS - Initializes with kmeans++.
    • l1_regularization
      Type: DOUBLE
      Provider name: l1Regularization
      Description: L1 regularization coefficient.
    • l2_regularization
      Type: DOUBLE
      Provider name: l2Regularization
      Description: L2 regularization coefficient.
    • learn_rate
      Type: DOUBLE
      Provider name: learnRate
      Description: Learning rate in training. Used only for iterative training algorithms.
    • learn_rate_strategy
      Type: STRING
      Provider name: learnRateStrategy
      Description: The strategy to determine learn rate for the current iteration.
      Possible values:
      • LEARN_RATE_STRATEGY_UNSPECIFIED
      • LINE_SEARCH - Use line search to determine learning rate.
      • CONSTANT - Use a constant learning rate.
    • loss_type
      Type: STRING
      Provider name: lossType
      Description: Type of loss function used during training run.
      Possible values:
      • LOSS_TYPE_UNSPECIFIED
      • MEAN_SQUARED_LOSS - Mean squared loss, used for linear regression.
      • MEAN_LOG_LOSS - Mean log loss, used for logistic regression.
    • max_iterations
      Type: INT64
      Provider name: maxIterations
      Description: The maximum number of iterations in training. Used only for iterative training algorithms.
    • max_parallel_trials
      Type: INT64
      Provider name: maxParallelTrials
      Description: Maximum number of trials to run in parallel.
    • max_time_series_length
      Type: INT64
      Provider name: maxTimeSeriesLength
      Description: Get truncated length by last n points in time series. Use separately from time_series_length_fraction and min_time_series_length.
    • max_tree_depth
      Type: INT64
      Provider name: maxTreeDepth
      Description: Maximum depth of a tree for boosted tree models.
    • min_relative_progress
      Type: DOUBLE
      Provider name: minRelativeProgress
      Description: When early_stop is true, stops training when accuracy improvement is less than ‘min_relative_progress’. Used only for iterative training algorithms.
    • min_split_loss
      Type: DOUBLE
      Provider name: minSplitLoss
      Description: Minimum split loss for boosted tree models.
    • min_time_series_length
      Type: INT64
      Provider name: minTimeSeriesLength
      Description: Set fast trend ARIMA_PLUS model minimum training length. Use in pair with time_series_length_fraction.
    • min_tree_child_weight
      Type: INT64
      Provider name: minTreeChildWeight
      Description: Minimum sum of instance weight needed in a child for boosted tree models.
    • model_uri
      Type: STRING
      Provider name: modelUri
      Description: Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
    • non_seasonal_order
      Type: STRUCT
      Provider name: nonSeasonalOrder
      Description: A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
      • d
        Type: INT64
        Provider name: d
        Description: Order of the differencing part.
      • p
        Type: INT64
        Provider name: p
        Description: Order of the autoregressive part.
      • q
        Type: INT64
        Provider name: q
        Description: Order of the moving-average part.
    • num_clusters
      Type: INT64
      Provider name: numClusters
      Description: Number of clusters for clustering models.
    • num_factors
      Type: INT64
      Provider name: numFactors
      Description: Num factors specified for matrix factorization models.
    • num_parallel_tree
      Type: INT64
      Provider name: numParallelTree
      Description: Number of parallel trees constructed during each iteration for boosted tree models.
    • num_trials
      Type: INT64
      Provider name: numTrials
      Description: Number of trials to run this hyperparameter tuning job.
    • optimization_strategy
      Type: STRING
      Provider name: optimizationStrategy
      Description: Optimization strategy for training linear regression models.
      Possible values:
      • OPTIMIZATION_STRATEGY_UNSPECIFIED
      • BATCH_GRADIENT_DESCENT - Uses an iterative batch gradient descent algorithm.
      • NORMAL_EQUATION - Uses a normal equation to solve linear regression problem.
    • sampled_shapley_num_paths
      Type: INT64
      Provider name: sampledShapleyNumPaths
      Description: Number of paths for the sampled Shapley explain method.
    • subsample
      Type: DOUBLE
      Provider name: subsample
      Description: Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
    • tf_version
      Type: STRING
      Provider name: tfVersion
      Description: Based on the selected TF version, the corresponding docker image is used to train external models.
    • time_series_data_column
      Type: STRING
      Provider name: timeSeriesDataColumn
      Description: Column to be designated as time series data for ARIMA model.
    • time_series_id_column
      Type: STRING
      Provider name: timeSeriesIdColumn
      Description: The time series id column that was used during ARIMA model training.
    • time_series_id_columns
      Type: UNORDERED_LIST_STRING
      Provider name: timeSeriesIdColumns
      Description: The time series id columns that were used during ARIMA model training.
    • time_series_length_fraction
      Type: DOUBLE
      Provider name: timeSeriesLengthFraction
      Description: Get truncated length by fraction in time series.
    • time_series_timestamp_column
      Type: STRING
      Provider name: timeSeriesTimestampColumn
      Description: Column to be designated as time series timestamp for ARIMA model.
    • tree_method
      Type: STRING
      Provider name: treeMethod
      Description: Tree construction algorithm for boosted tree models.
      Possible values:
      • TREE_METHOD_UNSPECIFIED - Unspecified tree method.
      • AUTO - Use heuristic to choose the fastest method.
      • EXACT - Exact greedy algorithm.
      • APPROX - Approximate greedy algorithm using quantile sketch and gradient histogram.
      • HIST - Fast histogram optimized approximate greedy algorithm.
    • trend_smoothing_window_size
      Type: INT64
      Provider name: trendSmoothingWindowSize
      Description: The smoothing window size for the trend component of the time series.
    • user_column
      Type: STRING
      Provider name: userColumn
      Description: User column specified for matrix factorization models.
    • wals_alpha
      Type: DOUBLE
      Provider name: walsAlpha
      Description: Hyperparameter for matrix factoration when implicit feedback type is specified.
    • warm_start
      Type: BOOLEAN
      Provider name: warmStart
      Description: Whether to train a model from the last checkpoint.
    • xgboost_version
      Type: STRING
      Provider name: xgboostVersion
      Description: User-selected XGBoost versions for training of XGBoost models.
  • start_time_ms
    Type: INT64
    Provider name: startTimeMs
    Description: Starting time of the trial.
  • training_loss
    Type: DOUBLE
    Provider name: trainingLoss
    Description: Loss computed on the training data at the end of trial.
  • trial_id
    Type: INT64
    Provider name: trialId
    Description: 1-based index of the trial.

label_columns

Type: UNORDERED_LIST_STRUCT
Provider name: labelColumns
Description: Output only. Label columns that were used to train this model. The output of the model will have a “predicted_” prefix to these columns.

  • name
    Type: STRING
    Provider name: name
    Description: Optional. The name of this field. Can be absent for struct fields.
  • type
    Type: STRUCT
    Provider name: type
    Description: Optional. The type of this parameter. Absent if not explicitly specified (e.g., CREATE FUNCTION statement can omit the return type; in this case the output parameter does not have this “type” field).

    • struct_type
      Type: STRUCT
      Provider name: structType
      Description: The fields of this struct, in order, if type_kind = “STRUCT”.

    • type_kind
      Type: STRING
      Provider name: typeKind
      Description: Required. The top level type of this field. Can be any GoogleSQL data type (e.g., “INT64”, “DATE”, “ARRAY”).
      Possible values:

      • TYPE_KIND_UNSPECIFIED - Invalid type.
      • INT64 - Encoded as a string in decimal format.
      • BOOL - Encoded as a boolean ‘false’ or ’true’.
      • FLOAT64 - Encoded as a number, or string ‘NaN
      • STRING - Infinity’ or ‘-Infinity’.
      • BYTES - Encoded as a string value.
      • TIMESTAMP - Encoded as a base64 string per RFC 4648, section 4.
      • DATE - Encoded as an RFC 3339 timestamp with mandatory ‘Z’ time zone string: 1985-04-12T23:20:50.52Z
      • TIME - Encoded as RFC 3339 full-date format string: 1985-04-12
      • DATETIME - Encoded as RFC 3339 partial-time format string: 23:20:50.52
      • INTERVAL - Encoded as RFC 3339 full-date ‘T’ partial-time: 1985-04-12T23:20:50.52
      • GEOGRAPHY - Encoded as fully qualified 3 part: 0-5 15 2:30:45.6
      • NUMERIC - Encoded as WKT
      • BIGNUMERIC - Encoded as a decimal string.
      • JSON - Encoded as a decimal string.
      • ARRAY - Encoded as a string.
      • STRUCT - Encoded as a list with types matching Type.array_type.

labels

Type: UNORDERED_LIST_STRING

last_modified_time

Type: INT64
Provider name: lastModifiedTime
Description: Output only. The time when this model was last modified, in millisecs since the epoch.

location

Type: STRING
Provider name: location
Description: Output only. The geographic location where the model resides. This value is inherited from the dataset.

model_reference

Type: STRUCT
Provider name: modelReference
Description: Required. Unique identifier for this model.

  • dataset_id
    Type: STRING
    Provider name: datasetId
    Description: Required. The ID of the dataset containing this model.
  • model_id
    Type: STRING
    Provider name: modelId
    Description: Required. The ID of the model. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
  • project_id
    Type: STRING
    Provider name: projectId
    Description: Required. The ID of the project containing this model.

model_type

Type: STRING
Provider name: modelType
Description: Output only. Type of the model resource.
Possible values:

  • MODEL_TYPE_UNSPECIFIED
  • LINEAR_REGRESSION - Linear regression model.
  • LOGISTIC_REGRESSION - Logistic regression based classification model.
  • KMEANS - K-means clustering model.
  • MATRIX_FACTORIZATION - Matrix factorization model.
  • DNN_CLASSIFIER - DNN classifier model.
  • TENSORFLOW - An imported TensorFlow model.
  • DNN_REGRESSOR - DNN regressor model.
  • XGBOOST - An imported XGBoost model.
  • BOOSTED_TREE_REGRESSOR - Boosted tree regressor model.
  • BOOSTED_TREE_CLASSIFIER - Boosted tree classifier model.
  • ARIMA - ARIMA model.
  • AUTOML_REGRESSOR - AutoML Tables regression model.
  • AUTOML_CLASSIFIER - AutoML Tables classification model.
  • PCA - Prinpical Component Analysis model.
  • DNN_LINEAR_COMBINED_CLASSIFIER - Wide-and-deep classifier model.
  • DNN_LINEAR_COMBINED_REGRESSOR - Wide-and-deep regressor model.
  • AUTOENCODER - Autoencoder model.
  • ARIMA_PLUS - New name for the ARIMA model.
  • ARIMA_PLUS_XREG - ARIMA with external regressors.
  • RANDOM_FOREST_REGRESSOR - Random forest regressor model.
  • RANDOM_FOREST_CLASSIFIER - Random forest classifier model.
  • TENSORFLOW_LITE - An imported TensorFlow Lite model.
  • ONNX - An imported ONNX model.

optimal_trial_ids

Type: UNORDERED_LIST_INT64
Provider name: optimalTrialIds
Description: Output only. For single-objective hyperparameter tuning models, it only contains the best trial. For multi-objective hyperparameter tuning models, it contains all Pareto optimal trials sorted by trial_id.

organization_id

Type: STRING

parent

Type: STRING

project_id

Type: STRING

project_number

Type: STRING

remote_model_info

Type: STRUCT
Provider name: remoteModelInfo
Description: Output only. Remote model info

  • connection
    Type: STRING
    Provider name: connection
    Description: Output only. Fully qualified name of the user-provided connection object of the remote model. Format: "projects/{project_id}/locations/{location_id}/connections/{connection_id}"
  • endpoint
    Type: STRING
    Provider name: endpoint
    Description: Output only. The endpoint for remote model.
  • max_batching_rows
    Type: INT64
    Provider name: maxBatchingRows
    Description: Output only. Max number of rows in each batch sent to the remote service. If unset, the number of rows in each batch is set dynamically.
  • remote_service_type
    Type: STRING
    Provider name: remoteServiceType
    Description: Output only. The remote service type for remote model.
    Possible values:

resource_name

Type: STRING

tags

Type: UNORDERED_LIST_STRING

training_runs

Type: UNORDERED_LIST_STRUCT
Provider name: trainingRuns
Description: Information for all training runs in increasing order of start_time.

  • class_level_global_explanations
    Type: UNORDERED_LIST_STRUCT
    Provider name: classLevelGlobalExplanations
    Description: Output only. Global explanation contains the explanation of top features on the class level. Applies to classification models only.
    • class_label
      Type: STRING
      Provider name: classLabel
      Description: Class label for this set of global explanations. Will be empty/null for binary logistic and linear regression models. Sorted alphabetically in descending order.
    • explanations
      Type: UNORDERED_LIST_STRUCT
      Provider name: explanations
      Description: A list of the top global explanations. Sorted by absolute value of attribution in descending order.
      • attribution
        Type: DOUBLE
        Provider name: attribution
        Description: Attribution of feature.
      • feature_name
        Type: STRING
        Provider name: featureName
        Description: The full feature name. For non-numerical features, will be formatted like .. Overall size of feature name will always be truncated to first 120 characters.
  • data_split_result
    Type: STRUCT
    Provider name: dataSplitResult
    Description: Output only. Data split result of the training run. Only set when the input data is actually split.
    • evaluation_table
      Type: STRUCT
      Provider name: evaluationTable
      Description: Table reference of the evaluation data after split.
      • dataset_id
        Type: STRING
        Provider name: datasetId
        Description: [Required] The ID of the dataset containing this table.
      • project_id
        Type: STRING
        Provider name: projectId
        Description: [Required] The ID of the project containing this table.
      • table_id
        Type: STRING
        Provider name: tableId
        Description: [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
    • test_table
      Type: STRUCT
      Provider name: testTable
      Description: Table reference of the test data after split.
      • dataset_id
        Type: STRING
        Provider name: datasetId
        Description: [Required] The ID of the dataset containing this table.
      • project_id
        Type: STRING
        Provider name: projectId
        Description: [Required] The ID of the project containing this table.
      • table_id
        Type: STRING
        Provider name: tableId
        Description: [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
    • training_table
      Type: STRUCT
      Provider name: trainingTable
      Description: Table reference of the training data after split.
      • dataset_id
        Type: STRING
        Provider name: datasetId
        Description: [Required] The ID of the dataset containing this table.
      • project_id
        Type: STRING
        Provider name: projectId
        Description: [Required] The ID of the project containing this table.
      • table_id
        Type: STRING
        Provider name: tableId
        Description: [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
  • evaluation_metrics
    Type: STRUCT
    Provider name: evaluationMetrics
    Description: Output only. The evaluation metrics over training/eval data that were computed at the end of training.
    • arima_forecasting_metrics
      Type: STRUCT
      Provider name: arimaForecastingMetrics
      Description: Populated for ARIMA models.
      • arima_fitting_metrics
        Type: UNORDERED_LIST_STRUCT
        Provider name: arimaFittingMetrics
        Description: Arima model fitting metrics.
        • aic
          Type: DOUBLE
          Provider name: aic
          Description: AIC.
        • log_likelihood
          Type: DOUBLE
          Provider name: logLikelihood
          Description: Log-likelihood.
        • variance
          Type: DOUBLE
          Provider name: variance
          Description: Variance.
      • arima_single_model_forecasting_metrics
        Type: UNORDERED_LIST_STRUCT
        Provider name: arimaSingleModelForecastingMetrics
        Description: Repeated as there can be many metric sets (one for each model) in auto-arima and the large-scale case.
        • arima_fitting_metrics
          Type: STRUCT
          Provider name: arimaFittingMetrics
          Description: Arima fitting metrics.
          • aic
            Type: DOUBLE
            Provider name: aic
            Description: AIC.
          • log_likelihood
            Type: DOUBLE
            Provider name: logLikelihood
            Description: Log-likelihood.
          • variance
            Type: DOUBLE
            Provider name: variance
            Description: Variance.
        • has_drift
          Type: BOOLEAN
          Provider name: hasDrift
          Description: Is arima model fitted with drift or not. It is always false when d is not 1.
        • has_holiday_effect
          Type: BOOLEAN
          Provider name: hasHolidayEffect
          Description: If true, holiday_effect is a part of time series decomposition result.
        • has_spikes_and_dips
          Type: BOOLEAN
          Provider name: hasSpikesAndDips
          Description: If true, spikes_and_dips is a part of time series decomposition result.
        • has_step_changes
          Type: BOOLEAN
          Provider name: hasStepChanges
          Description: If true, step_changes is a part of time series decomposition result.
        • non_seasonal_order
          Type: STRUCT
          Provider name: nonSeasonalOrder
          Description: Non-seasonal order.
          • d
            Type: INT64
            Provider name: d
            Description: Order of the differencing part.
          • p
            Type: INT64
            Provider name: p
            Description: Order of the autoregressive part.
          • q
            Type: INT64
            Provider name: q
            Description: Order of the moving-average part.
        • seasonal_periods
          Type: UNORDERED_LIST_STRING
          Provider name: seasonalPeriods
          Description: Seasonal periods. Repeated because multiple periods are supported for one time series.
        • time_series_id
          Type: STRING
          Provider name: timeSeriesId
          Description: The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
        • time_series_ids
          Type: UNORDERED_LIST_STRING
          Provider name: timeSeriesIds
          Description: The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
      • has_drift
        Type: UNORDERED_LIST_BOOLEAN
        Provider name: hasDrift
        Description: Whether Arima model fitted with drift or not. It is always false when d is not 1.
      • non_seasonal_order
        Type: UNORDERED_LIST_STRUCT
        Provider name: nonSeasonalOrder
        Description: Non-seasonal order.
        • d
          Type: INT64
          Provider name: d
          Description: Order of the differencing part.
        • p
          Type: INT64
          Provider name: p
          Description: Order of the autoregressive part.
        • q
          Type: INT64
          Provider name: q
          Description: Order of the moving-average part.
      • seasonal_periods
        Type: UNORDERED_LIST_STRING
        Provider name: seasonalPeriods
        Description: Seasonal periods. Repeated because multiple periods are supported for one time series.
      • time_series_id
        Type: UNORDERED_LIST_STRING
        Provider name: timeSeriesId
        Description: Id to differentiate different time series for the large-scale case.
    • binary_classification_metrics
      Type: STRUCT
      Provider name: binaryClassificationMetrics
      Description: Populated for binary classification/classifier models.
      • aggregate_classification_metrics
        Type: STRUCT
        Provider name: aggregateClassificationMetrics
        Description: Aggregate classification metrics.
        • accuracy
          Type: DOUBLE
          Provider name: accuracy
          Description: Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
        • f1_score
          Type: DOUBLE
          Provider name: f1Score
          Description: The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
        • log_loss
          Type: DOUBLE
          Provider name: logLoss
          Description: Logarithmic Loss. For multiclass this is a macro-averaged metric.
        • precision
          Type: DOUBLE
          Provider name: precision
          Description: Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier.
        • recall
          Type: DOUBLE
          Provider name: recall
          Description: Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
        • roc_auc
          Type: DOUBLE
          Provider name: rocAuc
          Description: Area Under a ROC Curve. For multiclass this is a macro-averaged metric.
        • threshold
          Type: DOUBLE
          Provider name: threshold
          Description: Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
      • binary_confusion_matrix_list
        Type: UNORDERED_LIST_STRUCT
        Provider name: binaryConfusionMatrixList
        Description: Binary confusion matrix at multiple thresholds.
        • accuracy
          Type: DOUBLE
          Provider name: accuracy
          Description: The fraction of predictions given the correct label.
        • f1_score
          Type: DOUBLE
          Provider name: f1Score
          Description: The equally weighted average of recall and precision.
        • false_negatives
          Type: INT64
          Provider name: falseNegatives
          Description: Number of false samples predicted as false.
        • false_positives
          Type: INT64
          Provider name: falsePositives
          Description: Number of false samples predicted as true.
        • positive_class_threshold
          Type: DOUBLE
          Provider name: positiveClassThreshold
          Description: Threshold value used when computing each of the following metric.
        • precision
          Type: DOUBLE
          Provider name: precision
          Description: The fraction of actual positive predictions that had positive actual labels.
        • recall
          Type: DOUBLE
          Provider name: recall
          Description: The fraction of actual positive labels that were given a positive prediction.
        • true_negatives
          Type: INT64
          Provider name: trueNegatives
          Description: Number of true samples predicted as false.
        • true_positives
          Type: INT64
          Provider name: truePositives
          Description: Number of true samples predicted as true.
      • negative_label
        Type: STRING
        Provider name: negativeLabel
        Description: Label representing the negative class.
      • positive_label
        Type: STRING
        Provider name: positiveLabel
        Description: Label representing the positive class.
    • clustering_metrics
      Type: STRUCT
      Provider name: clusteringMetrics
      Description: Populated for clustering models.
      • clusters
        Type: UNORDERED_LIST_STRUCT
        Provider name: clusters
        Description: Information for all clusters.
        • centroid_id
          Type: INT64
          Provider name: centroidId
          Description: Centroid id.
        • count
          Type: INT64
          Provider name: count
          Description: Count of training data rows that were assigned to this cluster.
        • feature_values
          Type: UNORDERED_LIST_STRUCT
          Provider name: featureValues
          Description: Values of highly variant features for this cluster.
          • categorical_value
            Type: STRUCT
            Provider name: categoricalValue
            Description: The categorical feature value.
            • category_counts
              Type: UNORDERED_LIST_STRUCT
              Provider name: categoryCounts
              Description: Counts of all categories for the categorical feature. If there are more than ten categories, we return top ten (by count) and return one more CategoryCount with category “OTHER” and count as aggregate counts of remaining categories.
              • category
                Type: STRING
                Provider name: category
                Description: The name of category.
              • count
                Type: INT64
                Provider name: count
                Description: The count of training samples matching the category within the cluster.
          • feature_column
            Type: STRING
            Provider name: featureColumn
            Description: The feature column name.
          • numerical_value
            Type: DOUBLE
            Provider name: numericalValue
            Description: The numerical feature value. This is the centroid value for this feature.
      • davies_bouldin_index
        Type: DOUBLE
        Provider name: daviesBouldinIndex
        Description: Davies-Bouldin index.
      • mean_squared_distance
        Type: DOUBLE
        Provider name: meanSquaredDistance
        Description: Mean of squared distances between each sample to its cluster centroid.
    • dimensionality_reduction_metrics
      Type: STRUCT
      Provider name: dimensionalityReductionMetrics
      Description: Evaluation metrics when the model is a dimensionality reduction model, which currently includes PCA.
      • total_explained_variance_ratio
        Type: DOUBLE
        Provider name: totalExplainedVarianceRatio
        Description: Total percentage of variance explained by the selected principal components.
    • multi_class_classification_metrics
      Type: STRUCT
      Provider name: multiClassClassificationMetrics
      Description: Populated for multi-class classification/classifier models.
      • aggregate_classification_metrics
        Type: STRUCT
        Provider name: aggregateClassificationMetrics
        Description: Aggregate classification metrics.
        • accuracy
          Type: DOUBLE
          Provider name: accuracy
          Description: Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
        • f1_score
          Type: DOUBLE
          Provider name: f1Score
          Description: The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
        • log_loss
          Type: DOUBLE
          Provider name: logLoss
          Description: Logarithmic Loss. For multiclass this is a macro-averaged metric.
        • precision
          Type: DOUBLE
          Provider name: precision
          Description: Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier.
        • recall
          Type: DOUBLE
          Provider name: recall
          Description: Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
        • roc_auc
          Type: DOUBLE
          Provider name: rocAuc
          Description: Area Under a ROC Curve. For multiclass this is a macro-averaged metric.
        • threshold
          Type: DOUBLE
          Provider name: threshold
          Description: Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
      • confusion_matrix_list
        Type: UNORDERED_LIST_STRUCT
        Provider name: confusionMatrixList
        Description: Confusion matrix at different thresholds.
        • confidence_threshold
          Type: DOUBLE
          Provider name: confidenceThreshold
          Description: Confidence threshold used when computing the entries of the confusion matrix.
        • rows
          Type: UNORDERED_LIST_STRUCT
          Provider name: rows
          Description: One row per actual label.
          • actual_label
            Type: STRING
            Provider name: actualLabel
            Description: The original label of this row.
          • entries
            Type: UNORDERED_LIST_STRUCT
            Provider name: entries
            Description: Info describing predicted label distribution.
            • item_count
              Type: INT64
              Provider name: itemCount
              Description: Number of items being predicted as this label.
            • predicted_label
              Type: STRING
              Provider name: predictedLabel
              Description: The predicted label. For confidence_threshold > 0, we will also add an entry indicating the number of items under the confidence threshold.
    • ranking_metrics
      Type: STRUCT
      Provider name: rankingMetrics
      Description: Populated for implicit feedback type matrix factorization models.
      • average_rank
        Type: DOUBLE
        Provider name: averageRank
        Description: Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank.
      • mean_average_precision
        Type: DOUBLE
        Provider name: meanAveragePrecision
        Description: Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users.
      • mean_squared_error
        Type: DOUBLE
        Provider name: meanSquaredError
        Description: Similar to the mean squared error computed in regression and explicit recommendation models except instead of computing the rating directly, the output from evaluate is computed against a preference which is 1 or 0 depending on if the rating exists or not.
      • normalized_discounted_cumulative_gain
        Type: DOUBLE
        Provider name: normalizedDiscountedCumulativeGain
        Description: A metric to determine the goodness of a ranking calculated from the predicted confidence by comparing it to an ideal rank measured by the original ratings.
    • regression_metrics
      Type: STRUCT
      Provider name: regressionMetrics
      Description: Populated for regression models and explicit feedback type matrix factorization models.
      • mean_absolute_error
        Type: DOUBLE
        Provider name: meanAbsoluteError
        Description: Mean absolute error.
      • mean_squared_error
        Type: DOUBLE
        Provider name: meanSquaredError
        Description: Mean squared error.
      • mean_squared_log_error
        Type: DOUBLE
        Provider name: meanSquaredLogError
        Description: Mean squared log error.
      • median_absolute_error
        Type: DOUBLE
        Provider name: medianAbsoluteError
        Description: Median absolute error.
      • r_squared
        Type: DOUBLE
        Provider name: rSquared
        Description: R^2 score. This corresponds to r2_score in ML.EVALUATE.
  • model_level_global_explanation
    Type: STRUCT
    Provider name: modelLevelGlobalExplanation
    Description: Output only. Global explanation contains the explanation of top features on the model level. Applies to both regression and classification models.
    • class_label
      Type: STRING
      Provider name: classLabel
      Description: Class label for this set of global explanations. Will be empty/null for binary logistic and linear regression models. Sorted alphabetically in descending order.
    • explanations
      Type: UNORDERED_LIST_STRUCT
      Provider name: explanations
      Description: A list of the top global explanations. Sorted by absolute value of attribution in descending order.
      • attribution
        Type: DOUBLE
        Provider name: attribution
        Description: Attribution of feature.
      • feature_name
        Type: STRING
        Provider name: featureName
        Description: The full feature name. For non-numerical features, will be formatted like .. Overall size of feature name will always be truncated to first 120 characters.
  • results
    Type: UNORDERED_LIST_STRUCT
    Provider name: results
    Description: Output only. Output of each iteration run, results.size() <= max_iterations.
    • duration_ms
      Type: INT64
      Provider name: durationMs
      Description: Time taken to run the iteration in milliseconds.
    • eval_loss
      Type: DOUBLE
      Provider name: evalLoss
      Description: Loss computed on the eval data at the end of iteration.
    • index
      Type: INT32
      Provider name: index
      Description: Index of the iteration, 0 based.
    • learn_rate
      Type: DOUBLE
      Provider name: learnRate
      Description: Learn rate used for this iteration.
    • training_loss
      Type: DOUBLE
      Provider name: trainingLoss
      Description: Loss computed on the training data at the end of iteration.
  • start_time
    Type: TIMESTAMP
    Provider name: startTime
    Description: Output only. The start time of this training run.
  • training_options
    Type: STRUCT
    Provider name: trainingOptions
    Description: Output only. Options that were used for this training run, includes user specified and default options that were used.
    • adjust_step_changes
      Type: BOOLEAN
      Provider name: adjustStepChanges
      Description: If true, detect step changes and make data adjustment in the input time series.
    • approx_global_feature_contrib
      Type: BOOLEAN
      Provider name: approxGlobalFeatureContrib
      Description: Whether to use approximate feature contribution method in XGBoost model explanation for global explain.
    • auto_arima
      Type: BOOLEAN
      Provider name: autoArima
      Description: Whether to enable auto ARIMA or not.
    • auto_arima_max_order
      Type: INT64
      Provider name: autoArimaMaxOrder
      Description: The max value of the sum of non-seasonal p and q.
    • auto_arima_min_order
      Type: INT64
      Provider name: autoArimaMinOrder
      Description: The min value of the sum of non-seasonal p and q.
    • batch_size
      Type: INT64
      Provider name: batchSize
      Description: Batch size for dnn models.
    • booster_type
      Type: STRING
      Provider name: boosterType
      Description: Booster type for boosted tree models.
      Possible values:
      • BOOSTER_TYPE_UNSPECIFIED - Unspecified booster type.
      • GBTREE - Gbtree booster.
      • DART - Dart booster.
    • calculate_p_values
      Type: BOOLEAN
      Provider name: calculatePValues
      Description: Whether or not p-value test should be computed for this model. Only available for linear and logistic regression models.
    • clean_spikes_and_dips
      Type: BOOLEAN
      Provider name: cleanSpikesAndDips
      Description: If true, clean spikes and dips in the input time series.
    • color_space
      Type: STRING
      Provider name: colorSpace
      Description: Enums for color space, used for processing images in Object Table. See more details at https://www.tensorflow.org/io/tutorials/colorspace.
      Possible values:
      • COLOR_SPACE_UNSPECIFIED - Unspecified color space
      • RGB - RGB
      • HSV - HSV
      • YIQ - YIQ
      • YUV - YUV
      • GRAYSCALE - GRAYSCALE
    • colsample_bylevel
      Type: DOUBLE
      Provider name: colsampleBylevel
      Description: Subsample ratio of columns for each level for boosted tree models.
    • colsample_bynode
      Type: DOUBLE
      Provider name: colsampleBynode
      Description: Subsample ratio of columns for each node(split) for boosted tree models.
    • colsample_bytree
      Type: DOUBLE
      Provider name: colsampleBytree
      Description: Subsample ratio of columns when constructing each tree for boosted tree models.
    • dart_normalize_type
      Type: STRING
      Provider name: dartNormalizeType
      Description: Type of normalization algorithm for boosted tree models using dart booster.
      Possible values:
      • DART_NORMALIZE_TYPE_UNSPECIFIED - Unspecified dart normalize type.
      • TREE - New trees have the same weight of each of dropped trees.
      • FOREST - New trees have the same weight of sum of dropped trees.
    • data_frequency
      Type: STRING
      Provider name: dataFrequency
      Description: The data frequency of a time series.
      Possible values:
      • DATA_FREQUENCY_UNSPECIFIED
      • AUTO_FREQUENCY - Automatically inferred from timestamps.
      • YEARLY - Yearly data.
      • QUARTERLY - Quarterly data.
      • MONTHLY - Monthly data.
      • WEEKLY - Weekly data.
      • DAILY - Daily data.
      • HOURLY - Hourly data.
      • PER_MINUTE - Per-minute data.
    • data_split_column
      Type: STRING
      Provider name: dataSplitColumn
      Description: The column to split data with. This column won’t be used as a feature. 1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data. 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
    • data_split_eval_fraction
      Type: DOUBLE
      Provider name: dataSplitEvalFraction
      Description: The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.
    • data_split_method
      Type: STRING
      Provider name: dataSplitMethod
      Description: The data split type for training and evaluation, e.g. RANDOM.
      Possible values:
      • DATA_SPLIT_METHOD_UNSPECIFIED
      • RANDOM - Splits data randomly.
      • CUSTOM - Splits data with the user provided tags.
      • SEQUENTIAL - Splits data sequentially.
      • NO_SPLIT - Data split will be skipped.
      • AUTO_SPLIT - Splits data automatically: Uses NO_SPLIT if the data size is small. Otherwise uses RANDOM.
    • decompose_time_series
      Type: BOOLEAN
      Provider name: decomposeTimeSeries
      Description: If true, perform decompose time series and save the results.
    • distance_type
      Type: STRING
      Provider name: distanceType
      Description: Distance type for clustering models.
      Possible values:
      • DISTANCE_TYPE_UNSPECIFIED
      • EUCLIDEAN - Eculidean distance.
      • COSINE - Cosine distance.
    • dropout
      Type: DOUBLE
      Provider name: dropout
      Description: Dropout probability for dnn models.
    • early_stop
      Type: BOOLEAN
      Provider name: earlyStop
      Description: Whether to stop early when the loss doesn’t improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms.
    • enable_global_explain
      Type: BOOLEAN
      Provider name: enableGlobalExplain
      Description: If true, enable global explanation during training.
    • feedback_type
      Type: STRING
      Provider name: feedbackType
      Description: Feedback type that specifies which algorithm to run for matrix factorization.
      Possible values:
      • FEEDBACK_TYPE_UNSPECIFIED
      • IMPLICIT - Use weighted-als for implicit feedback problems.
      • EXPLICIT - Use nonweighted-als for explicit feedback problems.
    • hidden_units
      Type: UNORDERED_LIST_INT64
      Provider name: hiddenUnits
      Description: Hidden units for dnn models.
    • holiday_region
      Type: STRING
      Provider name: holidayRegion
      Description: The geographical region based on which the holidays are considered in time series modeling. If a valid value is specified, then holiday effects modeling is enabled.
      Possible values:
      • HOLIDAY_REGION_UNSPECIFIED - Holiday region unspecified.
      • GLOBAL - Global.
      • NA - North America.
      • JAPAC - Japan and Asia Pacific: Korea, Greater China, India, Australia, and New Zealand.
      • EMEA - Europe, the Middle East and Africa.
      • LAC - Latin America and the Caribbean.
      • AE - United Arab Emirates
      • AR - Argentina
      • AT - Austria
      • AU - Australia
      • BE - Belgium
      • BR - Brazil
      • CA - Canada
      • CH - Switzerland
      • CL - Chile
      • CN - China
      • CO - Colombia
      • CS - Czechoslovakia
      • CZ - Czech Republic
      • DE - Germany
      • DK - Denmark
      • DZ - Algeria
      • EC - Ecuador
      • EE - Estonia
      • EG - Egypt
      • ES - Spain
      • FI - Finland
      • FR - France
      • GB - Great Britain (United Kingdom)
      • GR - Greece
      • HK - Hong Kong
      • HU - Hungary
      • ID - Indonesia
      • IE - Ireland
      • IL - Israel
      • IN - India
      • IR - Iran
      • IT - Italy
      • JP - Japan
      • KR - Korea (South)
      • LV - Latvia
      • MA - Morocco
      • MX - Mexico
      • MY - Malaysia
      • NG - Nigeria
      • NL - Netherlands
      • NO - Norway
      • NZ - New Zealand
      • PE - Peru
      • PH - Philippines
      • PK - Pakistan
      • PL - Poland
      • PT - Portugal
      • RO - Romania
      • RS - Serbia
      • RU - Russian Federation
      • SA - Saudi Arabia
      • SE - Sweden
      • SG - Singapore
      • SI - Slovenia
      • SK - Slovakia
      • TH - Thailand
      • TR - Turkey
      • TW - Taiwan
      • UA - Ukraine
      • US - United States
      • VE - Venezuela
      • VN - Viet Nam
      • ZA - South Africa
    • horizon
      Type: INT64
      Provider name: horizon
      Description: The number of periods ahead that need to be forecasted.
    • hparam_tuning_objectives
      Type: UNORDERED_LIST_STRING
      Provider name: hparamTuningObjectives
      Description: The target evaluation metrics to optimize the hyperparameters for.
    • include_drift
      Type: BOOLEAN
      Provider name: includeDrift
      Description: Include drift when fitting an ARIMA model.
    • initial_learn_rate
      Type: DOUBLE
      Provider name: initialLearnRate
      Description: Specifies the initial learning rate for the line search learn rate strategy.
    • input_label_columns
      Type: UNORDERED_LIST_STRING
      Provider name: inputLabelColumns
      Description: Name of input label columns in training data.
    • instance_weight_column
      Type: STRING
      Provider name: instanceWeightColumn
      Description: Name of the instance weight column for training data. This column isn’t be used as a feature.
    • integrated_gradients_num_steps
      Type: INT64
      Provider name: integratedGradientsNumSteps
      Description: Number of integral steps for the integrated gradients explain method.
    • item_column
      Type: STRING
      Provider name: itemColumn
      Description: Item column specified for matrix factorization models.
    • kmeans_initialization_column
      Type: STRING
      Provider name: kmeansInitializationColumn
      Description: The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
    • kmeans_initialization_method
      Type: STRING
      Provider name: kmeansInitializationMethod
      Description: The method used to initialize the centroids for kmeans algorithm.
      Possible values:
      • KMEANS_INITIALIZATION_METHOD_UNSPECIFIED - Unspecified initialization method.
      • RANDOM - Initializes the centroids randomly.
      • CUSTOM - Initializes the centroids using data specified in kmeans_initialization_column.
      • KMEANS_PLUS_PLUS - Initializes with kmeans++.
    • l1_regularization
      Type: DOUBLE
      Provider name: l1Regularization
      Description: L1 regularization coefficient.
    • l2_regularization
      Type: DOUBLE
      Provider name: l2Regularization
      Description: L2 regularization coefficient.
    • learn_rate
      Type: DOUBLE
      Provider name: learnRate
      Description: Learning rate in training. Used only for iterative training algorithms.
    • learn_rate_strategy
      Type: STRING
      Provider name: learnRateStrategy
      Description: The strategy to determine learn rate for the current iteration.
      Possible values:
      • LEARN_RATE_STRATEGY_UNSPECIFIED
      • LINE_SEARCH - Use line search to determine learning rate.
      • CONSTANT - Use a constant learning rate.
    • loss_type
      Type: STRING
      Provider name: lossType
      Description: Type of loss function used during training run.
      Possible values:
      • LOSS_TYPE_UNSPECIFIED
      • MEAN_SQUARED_LOSS - Mean squared loss, used for linear regression.
      • MEAN_LOG_LOSS - Mean log loss, used for logistic regression.
    • max_iterations
      Type: INT64
      Provider name: maxIterations
      Description: The maximum number of iterations in training. Used only for iterative training algorithms.
    • max_parallel_trials
      Type: INT64
      Provider name: maxParallelTrials
      Description: Maximum number of trials to run in parallel.
    • max_time_series_length
      Type: INT64
      Provider name: maxTimeSeriesLength
      Description: Get truncated length by last n points in time series. Use separately from time_series_length_fraction and min_time_series_length.
    • max_tree_depth
      Type: INT64
      Provider name: maxTreeDepth
      Description: Maximum depth of a tree for boosted tree models.
    • min_relative_progress
      Type: DOUBLE
      Provider name: minRelativeProgress
      Description: When early_stop is true, stops training when accuracy improvement is less than ‘min_relative_progress’. Used only for iterative training algorithms.
    • min_split_loss
      Type: DOUBLE
      Provider name: minSplitLoss
      Description: Minimum split loss for boosted tree models.
    • min_time_series_length
      Type: INT64
      Provider name: minTimeSeriesLength
      Description: Set fast trend ARIMA_PLUS model minimum training length. Use in pair with time_series_length_fraction.
    • min_tree_child_weight
      Type: INT64
      Provider name: minTreeChildWeight
      Description: Minimum sum of instance weight needed in a child for boosted tree models.
    • model_uri
      Type: STRING
      Provider name: modelUri
      Description: Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
    • non_seasonal_order
      Type: STRUCT
      Provider name: nonSeasonalOrder
      Description: A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
      • d
        Type: INT64
        Provider name: d
        Description: Order of the differencing part.
      • p
        Type: INT64
        Provider name: p
        Description: Order of the autoregressive part.
      • q
        Type: INT64
        Provider name: q
        Description: Order of the moving-average part.
    • num_clusters
      Type: INT64
      Provider name: numClusters
      Description: Number of clusters for clustering models.
    • num_factors
      Type: INT64
      Provider name: numFactors
      Description: Num factors specified for matrix factorization models.
    • num_parallel_tree
      Type: INT64
      Provider name: numParallelTree
      Description: Number of parallel trees constructed during each iteration for boosted tree models.
    • num_trials
      Type: INT64
      Provider name: numTrials
      Description: Number of trials to run this hyperparameter tuning job.
    • optimization_strategy
      Type: STRING
      Provider name: optimizationStrategy
      Description: Optimization strategy for training linear regression models.
      Possible values:
      • OPTIMIZATION_STRATEGY_UNSPECIFIED
      • BATCH_GRADIENT_DESCENT - Uses an iterative batch gradient descent algorithm.
      • NORMAL_EQUATION - Uses a normal equation to solve linear regression problem.
    • sampled_shapley_num_paths
      Type: INT64
      Provider name: sampledShapleyNumPaths
      Description: Number of paths for the sampled Shapley explain method.
    • subsample
      Type: DOUBLE
      Provider name: subsample
      Description: Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
    • tf_version
      Type: STRING
      Provider name: tfVersion
      Description: Based on the selected TF version, the corresponding docker image is used to train external models.
    • time_series_data_column
      Type: STRING
      Provider name: timeSeriesDataColumn
      Description: Column to be designated as time series data for ARIMA model.
    • time_series_id_column
      Type: STRING
      Provider name: timeSeriesIdColumn
      Description: The time series id column that was used during ARIMA model training.
    • time_series_id_columns
      Type: UNORDERED_LIST_STRING
      Provider name: timeSeriesIdColumns
      Description: The time series id columns that were used during ARIMA model training.
    • time_series_length_fraction
      Type: DOUBLE
      Provider name: timeSeriesLengthFraction
      Description: Get truncated length by fraction in time series.
    • time_series_timestamp_column
      Type: STRING
      Provider name: timeSeriesTimestampColumn
      Description: Column to be designated as time series timestamp for ARIMA model.
    • tree_method
      Type: STRING
      Provider name: treeMethod
      Description: Tree construction algorithm for boosted tree models.
      Possible values:
      • TREE_METHOD_UNSPECIFIED - Unspecified tree method.
      • AUTO - Use heuristic to choose the fastest method.
      • EXACT - Exact greedy algorithm.
      • APPROX - Approximate greedy algorithm using quantile sketch and gradient histogram.
      • HIST - Fast histogram optimized approximate greedy algorithm.
    • trend_smoothing_window_size
      Type: INT64
      Provider name: trendSmoothingWindowSize
      Description: The smoothing window size for the trend component of the time series.
    • user_column
      Type: STRING
      Provider name: userColumn
      Description: User column specified for matrix factorization models.
    • wals_alpha
      Type: DOUBLE
      Provider name: walsAlpha
      Description: Hyperparameter for matrix factoration when implicit feedback type is specified.
    • warm_start
      Type: BOOLEAN
      Provider name: warmStart
      Description: Whether to train a model from the last checkpoint.
    • xgboost_version
      Type: STRING
      Provider name: xgboostVersion
      Description: User-selected XGBoost versions for training of XGBoost models.
  • training_start_time
    Type: INT64
    Provider name: trainingStartTime
    Description: Output only. The start time of this training run, in milliseconds since epoch.
  • vertex_ai_model_id
    Type: STRING
    Provider name: vertexAiModelId
    Description: The model id in the Vertex AI Model Registry for this training run.
  • vertex_ai_model_version
    Type: STRING
    Provider name: vertexAiModelVersion
    Description: Output only. The model version in the Vertex AI Model Registry for this training run.