google_api_big_query v0.40.0 GoogleApi.BigQuery.V2.Model.TrainingOptions View Source
Attributes
batchSize
(type:String.t
, default:nil
) - Batch size for dnn models.dataSplitColumn
(type:String.t
, default:nil
) - The column to split data with. This column won't be used as a feature.- 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.
- 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
dataSplitEvalFraction
(type:float()
, default:nil
) - 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.dataSplitMethod
(type:String.t
, default:nil
) - The data split type for training and evaluation, e.g. RANDOM.distanceType
(type:String.t
, default:nil
) - Distance type for clustering models.dropout
(type:float()
, default:nil
) - Dropout probability for dnn models.earlyStop
(type:boolean()
, default:nil
) - Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms.feedbackType
(type:String.t
, default:nil
) - Feedback type that specifies which algorithm to run for matrix factorization.hiddenUnits
(type:list(String.t)
, default:nil
) - Hidden units for dnn models.initialLearnRate
(type:float()
, default:nil
) - Specifies the initial learning rate for the line search learn rate strategy.inputLabelColumns
(type:list(String.t)
, default:nil
) - Name of input label columns in training data.itemColumn
(type:String.t
, default:nil
) - Item column specified for matrix factorization models.kmeansInitializationColumn
(type:String.t
, default:nil
) - The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.kmeansInitializationMethod
(type:String.t
, default:nil
) - The method used to initialize the centroids for kmeans algorithm.l1Regularization
(type:float()
, default:nil
) - L1 regularization coefficient.l2Regularization
(type:float()
, default:nil
) - L2 regularization coefficient.labelClassWeights
(type:map()
, default:nil
) - Weights associated with each label class, for rebalancing the training data. Only applicable for classification models.learnRate
(type:float()
, default:nil
) - Learning rate in training. Used only for iterative training algorithms.learnRateStrategy
(type:String.t
, default:nil
) - The strategy to determine learn rate for the current iteration.lossType
(type:String.t
, default:nil
) - Type of loss function used during training run.maxIterations
(type:String.t
, default:nil
) - The maximum number of iterations in training. Used only for iterative training algorithms.maxTreeDepth
(type:String.t
, default:nil
) - Maximum depth of a tree for boosted tree models.minRelativeProgress
(type:float()
, default:nil
) - When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.minSplitLoss
(type:float()
, default:nil
) - Minimum split loss for boosted tree models.modelUri
(type:String.t
, default:nil
) - [Beta] Google Cloud Storage URI from which the model was imported. Only applicable for imported models.numClusters
(type:String.t
, default:nil
) - Number of clusters for clustering models.numFactors
(type:String.t
, default:nil
) - Num factors specified for matrix factorization models.optimizationStrategy
(type:String.t
, default:nil
) - Optimization strategy for training linear regression models.subsample
(type:float()
, default:nil
) - Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.userColumn
(type:String.t
, default:nil
) - User column specified for matrix factorization models.walsAlpha
(type:float()
, default:nil
) - Hyperparameter for matrix factoration when implicit feedback type is specified.warmStart
(type:boolean()
, default:nil
) - Whether to train a model from the last checkpoint.
Link to this section Summary
Functions
Unwrap a decoded JSON object into its complex fields.
Link to this section Types
Specs
t() :: %GoogleApi.BigQuery.V2.Model.TrainingOptions{ batchSize: String.t(), dataSplitColumn: String.t(), dataSplitEvalFraction: float(), dataSplitMethod: String.t(), distanceType: String.t(), dropout: float(), earlyStop: boolean(), feedbackType: String.t(), hiddenUnits: [String.t()], initialLearnRate: float(), inputLabelColumns: [String.t()], itemColumn: String.t(), kmeansInitializationColumn: String.t(), kmeansInitializationMethod: String.t(), l1Regularization: float(), l2Regularization: float(), labelClassWeights: map(), learnRate: float(), learnRateStrategy: String.t(), lossType: String.t(), maxIterations: String.t(), maxTreeDepth: String.t(), minRelativeProgress: float(), minSplitLoss: float(), modelUri: String.t(), numClusters: String.t(), numFactors: String.t(), optimizationStrategy: String.t(), subsample: float(), userColumn: String.t(), walsAlpha: float(), warmStart: boolean() }
Link to this section Functions
Specs
Unwrap a decoded JSON object into its complex fields.