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The CREATE MODEL statement for boosted tree models using XGBoost | BigQuery

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This document describes the CREATE MODEL statement for creating boosted tree models in BigQuery by using SQL. Alternatively, you can use the Google Cloud console user interface to create a model by using a UI (Preview) instead of constructing the SQL statement yourself. Boosted tree models are trained using the XGBoost library.

You can use boosted tree regressor models with the ML.PREDICT function to perform regression, and you can use boosted tree classifier models with the ML.PREDICT function to perform classification. You can use both types of boosted tree models with the ML.PREDICT function to perform anomaly detection.

For information about the supported SQL statements and functions for each model type, see End-to-end user journey for each model.

{CREATE MODEL | CREATE MODEL IF NOT EXISTS | CREATE OR REPLACE MODEL} model_name
OPTIONS(model_option_list)
AS query_statement

model_option_list:
MODEL_TYPE = { 'BOOSTED_TREE_CLASSIFIER' | 'BOOSTED_TREE_REGRESSOR' }
    [, APPROX_GLOBAL_FEATURE_CONTRIB = { TRUE | FALSE } ]
    [, CATEGORY_ENCODING_METHOD = { 'ONE_HOT_ENCODING` | 'TARGET_ENCODING' | 'LABEL_ENCODING' } ]
    [, BOOSTER_TYPE = { {'GBTREE' | 'DART'} | HPARAM_CANDIDATES([candidates]) } ]
    [, DART_NORMALIZE_TYPE = { {'TREE' | 'FOREST'} | HPARAM_CANDIDATES([candidates]) } ]
    [, NUM_PARALLEL_TREE = { int64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, MAX_TREE_DEPTH = { int64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, DROPOUT = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, L1_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, L2_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, LEARN_RATE = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, TREE_METHOD = { {'AUTO' | 'EXACT' | 'APPROX' | 'HIST'} | HPARAM_CANDIDATES([candidates]) } ]
    [, MIN_TREE_CHILD_WEIGHT = { int64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, COLSAMPLE_BYTREE = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, COLSAMPLE_BYLEVEL = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, COLSAMPLE_BYNODE = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, MIN_SPLIT_LOSS = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, SUBSAMPLE = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, INSTANCE_WEIGHT_COL = string_value ]
    [, XGBOOST_VERSION = { '0.9' | '1.1' } ]
    [, AUTO_CLASS_WEIGHTS = { TRUE | FALSE } ]
    [, CLASS_WEIGHTS = struct_array ]
    [, ENABLE_GLOBAL_EXPLAIN = { TRUE | FALSE } ]
    [, EARLY_STOP = { TRUE | FALSE } ]
    [, MIN_REL_PROGRESS = float64_value ]
    [, INPUT_LABEL_COLS = string_array ]
    [, MAX_ITERATIONS = int64_value ]
    [, DATA_SPLIT_METHOD = { 'AUTO_SPLIT' | 'RANDOM' | 'CUSTOM' | 'SEQ' | 'NO_SPLIT' } ]
    [, DATA_SPLIT_EVAL_FRACTION = float64_value ]
    [, DATA_SPLIT_TEST_FRACTION = float64_value ]
    [, DATA_SPLIT_COL = string_value ]
    [, NUM_TRIALS = int64_value ]
    [, MAX_PARALLEL_TRIALS = int64_value ]
    [, HPARAM_TUNING_ALGORITHM = { 'VIZIER_DEFAULT' | 'RANDOM_SEARCH' | 'GRID_SEARCH' } ]
    [, HPARAM_TUNING_OBJECTIVES = { 'ROC_AUC' | 'R2_SCORE' | ... } ]
    [, KMS_KEY_NAME = string_value ]

Creates and trains a new model in the specified dataset. If the model name exists, CREATE MODEL returns an error.

Creates and trains a new model only if the model doesn't exist in the specified dataset.

Creates and trains a model and replaces an existing model with the same name in the specified dataset.

The name of the model you're creating or replacing. The model name must be unique in the dataset: no other model or table can have the same name. The model name must follow the same naming rules as a BigQuery table. A model name can:

• Contain up to 1,024 characters
• Contain letters (upper or lower case), numbers, and underscores

model_name is not case-sensitive.

If you don't have a default project configured, then you must prepend the project ID to the model name in the following format, including backticks:

`[PROJECT_ID].[DATASET].[MODEL]`

For example, `myproject.mydataset.mymodel`.

Syntax

MODEL_TYPE = { 'BOOSTED_TREE_CLASSIFIER' | 'BOOSTED_TREE_REGRESSOR' }

Description

Specifies the model type. This option is required.

Syntax

APPROX_GLOBAL_FEATURE_CONTRIB = { TRUE | FALSE }

Description

Enables fast approximation for feature contributions. This capability is provided by the XGBoost library; BigQuery ML only passes this option through to it. For more information, see Package 'xgboost' and search for approxcontrib.

In order to use the fast approximation for feature contribution computations, you need to set both ENABLE_GLOBAL_EXPLAIN and APPROX_GLOBAL_FEATURE_CONTRIB to TRUE.

Arguments

A BOOL value. The default value is TRUE when ENABLE_GLOBAL_EXPLAIN is TRUE and NUM_PARALLEL_TREE >= 10, otherwise it is FALSE.

Syntax

CATEGORY_ENCODING_METHOD = { 'ONE_HOT_ENCODING' | 'TARGET_ENCODING' | 'LABEL_ENCODING' }

Description

Specifies which encoding method to use on non-numeric features. For more information about supported encoding methods, see BigQuery ML auto preprocessing.

Arguments

This option accepts the following values:

• LABEL_ENCODING. This is the default.
• TARGET_ENCODING
• ONE_HOT_ENCODING

Syntax

BOOSTER_TYPE = { { 'GBTREE' | 'DART'} | HPARAM_CANDIDATES([candidates]) }

Description

Specifies the booster type to use.

Arguments

This option accepts the following values:

• GBTREE: tree-booster. This is the default.
• DART: dart-booster

If you are running hyperparameter training, you can provide more than one value for this option by using HPARAM_CANDIDATES and specifying an array. For example, BOOSTER_TYPE = HPARAM_CANDIDATES(['GBTREE', 'DART']).

Syntax

DART_NORMALIZE_TYPE = { { 'TREE' | 'FOREST'} | HPARAM_CANDIDATES([candidates]) }

Description

The type of normalization algorithm to use if you are using the DART booster.

Arguments

This option accepts the following values:

• TREE: New trees have the same weight of each of the dropped trees. This is the default value.
• FOREST: New trees have the same weight of the sum of the dropped trees (forest).

If you are running hyperparameter training, you can provide more than one value for this option by using HPARAM_CANDIDATES and specifying an array. For example, DART_NORMALIZE_TYPE = HPARAM_CANDIDATES(['TREE', 'FOREST']).

Syntax

NUM_PARALLEL_TREE = { int64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

The number of parallel trees constructed during each iteration. To train a boosted random forest model, set this value to larger than 1.

Arguments

If you aren't running hyperparameter tuning, then you can specify an INT64 value. The default value is 1.

If you are running hyperparameter tuning, use one of the following options:

• The HPARAM_RANGE keyword and two INT64 values that define the range of the hyperparameter. For example, NUM_PARALLEL_TREE = HPARAM_RANGE(1, 5).
• The HPARAM_CANDIDATES keyword and an array of INT64 values that provide discrete values to use for the hyperparameter. For example, NUM_PARALLEL_TREE = HPARAM_CANDIDATES([0, 1, 3, 5]).

When running hyperparameter tuning, the valid range is (1, ∞], there is no default range, and the scale type is LINEAR.

Syntax

MAX_TREE_DEPTH = { int64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

The maximum depth of a tree.

Arguments

If you aren't running hyperparameter tuning, then you can specify an INT64 value. The default value is 6.

If you are running hyperparameter tuning, use one of the following options:

• The HPARAM_RANGE keyword and two INT64 values that define the range of the hyperparameter. For example, MAX_TREE_DEPTH = HPARAM_RANGE(0, 4).
• The HPARAM_CANDIDATES keyword and an array of INT64 values that provide discrete values to use for the hyperparameter. For example, MAX_TREE_DEPTH = HPARAM_CANDIDATES([1, 5, 10, 15]).

When running hyperparameter tuning, the valid range is (1, 20], the default range is (1, 10], and the scale type is LINEAR.

Syntax

DROPOUT = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

Specifies the dropout rate, which is the fraction of previous trees to drop during the dropout.

Arguments

If you aren't running hyperparameter tuning, then you can specify a FLOAT64 value between 0 and 1.0. The default value is 0.

If you are running hyperparameter tuning, use one of the following options:

• The HPARAM_RANGE keyword and two FLOAT64 values that define the range of the hyperparameter. For example, DROPOUT = HPARAM_RANGE(0, 0.6).
• The HPARAM_CANDIDATES keyword and an array of FLOAT64 values that provide discrete values to use for the hyperparameter. For example, DROPOUT = HPARAM_CANDIDATES([0, 0.1, 0.2, 0.6]).

When running hyperparameter tuning, the valid range is (0, 1.0], there is no default range, and the scale type is LINEAR.

Syntax

L1_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

The amount of L1 regularization applied.

Arguments

If you aren't running hyperparameter tuning, then you can specify a FLOAT64 value. The default value is 0.

If you are running hyperparameter tuning, then you can use one of the following options:

• The HPARAM_RANGE keyword and two FLOAT64 values that define the range to use for the hyperparameter. For example, L1_REG = HPARAM_RANGE(0, 5.0).
• The HPARAM_CANDIDATES keyword and an array of FLOAT64 values that provide discrete values to use for the hyperparameter. For example, L1_REG = HPARAM_CANDIDATES([0, 1.0, 3.0, 5.0]).

When running hyperparameter tuning, the valid range is (0, ∞), the default range is (0, 10.0], and the scale type is LOG.

Syntax

L2_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

The amount of L2 regularization applied.

Arguments

If you aren't running hyperparameter tuning, then you can specify a FLOAT64 value. The default value is 1.0.

If you are running hyperparameter tuning, then you can use one of the following options:

• The HPARAM_RANGE keyword and two FLOAT64 values that define the range to use for the hyperparameter. For example, L2_REG = HPARAM_RANGE(1.5, 5.0).
• The HPARAM_CANDIDATES keyword and an array of FLOAT64 values that provide discrete values to use for the hyperparameter. For example, L2_REG = HPARAM_CANDIDATES([0, 1.0, 3.0, 5.0]).

When running hyperparameter tuning, the valid range is (0, ∞), the default range is (0, 10.0], and the scale type is LOG.

Syntax

LEARN_RATE = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

LEARN_RATE is the step size shrinkage used in updates to prevents overfitting. After each boosting step, LEARN_RATE shrinks the feature weights to make the boosting process more conservative.

Arguments

If you aren't running hyperparameter tuning, then you can specify a FLOAT64 value. The default value is 0.3.

If you are running hyperparameter tuning, use one of the following options:

• The HPARAM_RANGE keyword and two FLOAT64 values that define the range of the hyperparameter. For example, LEARN_RATE = HPARAM_RANGE(0, 0.5).
• The HPARAM_CANDIDATES keyword and an array of FLOAT64 values that provide discrete values to use for the hyperparameter. For example, LEARN_RATE = HPARAM_CANDIDATES([0, 0.1, 0.3, 0.5]).

When running hyperparameter tuning, the valid range is (0, ∞], the default range is (0, 1.0], and the scale type is LINEAR.

Syntax

TREE_METHOD = { { 'AUTO' | 'EXACT' | 'APPROX' | 'HIST'} | HPARAM_CANDIDATES([candidates]) }

Description

The type of tree construction algorithm.

HIST is recommended for large datasets in order to increase training speed and reduce resource consumption. For more information, see tree booster.

Arguments

This option accepts the following values:

• AUTO: Faster histogram optimized approximate greedy algorithm. This is the default.
• EXACT: Exact greedy algorithm. Enumerates all split candidates.
• APPROX: Approximate greedy algorithm using quantile sketch and gradient histogram.
• HIST: Same as the AUTO tree method..

If you are running hyperparameter training, you can provide more than one value for this option by using HPARAM_CANDIDATES and specifying an array. For example, TREE_METHOD = HPARAM_CANDIDATES(['APPROX', 'HIST']).

Syntax

MIN_TREE_CHILD_WEIGHT = { int64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

The minimum sum of instance weight needed in a child for further partitioning. If the tree partition step results in a leaf node whose sum of instance weight is less than MIN_TREE_CHILD_WEIGHT, then the building process stops partitioning. The larger the MIN_TREE_CHILD_WEIGHT value is, the more conservative the algorithm is.

Arguments

If you aren't running hyperparameter tuning, then you can specify an INT64 value greater than or equal to 0. The default value is 1.

If you are running hyperparameter tuning, use one of the following options:

• The HPARAM_RANGE keyword and two INT64 values that define the range of the hyperparameter. For example, MIN_TREE_CHILD_WEIGHT = HPARAM_RANGE(0, 5).
• The HPARAM_CANDIDATES keyword and an array of INT64 values that provide discrete values to use for the hyperparameter. For example, MIN_TREE_CHILD_WEIGHT = HPARAM_CANDIDATES([0, 1, 3, 5]).

When running hyperparameter tuning, the valid range is [0, ∞), there is no default range, and the scale type is LINEAR.

Syntax

COLSAMPLE_BYTREE = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

The subsample ratio of columns when constructing each tree. Subsampling occurs once for every tree constructed.

Arguments

If you aren't running hyperparameter tuning, then you can specify a FLOAT64 value between 0 and 1.0. The default value is 1.0.

If you are running hyperparameter tuning, use one of the following options:

• The HPARAM_RANGE keyword and two FLOAT64 values that define the range of the hyperparameter. For example, COLSAMPLE_BYTREE = HPARAM_RANGE(0, 0.3)).
• The HPARAM_CANDIDATES keyword and an array of FLOAT64 values that provide discrete values to use for the hyperparameter. For example, COLSAMPLE_BYTREE = HPARAM_CANDIDATES([0, 0.1, 0.3, 0.5]).

When running hyperparameter tuning, the valid range is [0, 1.0], there is no default range, and the scale type is LINEAR.

Syntax

COLSAMPLE_BYLEVEL = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

The subsample ratio of columns for each level. Subsampling occurs once for every new depth level reached in a tree. Columns are subsampled from the set of columns chosen for the current tree.

Arguments

If you aren't running hyperparameter tuning, then you can specify a FLOAT64 value between 0 and 1.0. The default value is 1.0.

If you are running hyperparameter tuning, use one of the following options:

• The HPARAM_RANGE keyword and two FLOAT64 values that define the range of the hyperparameter. For example, COLSAMPLE_BYLEVEL = HPARAM_RANGE(0, 0.3)).
• The HPARAM_CANDIDATES keyword and an array of FLOAT64 values that provide discrete values to use for the hyperparameter. For example, COLSAMPLE_BYLEVEL = HPARAM_CANDIDATES([0, 0.1, 0.3, 0.5]).

When running hyperparameter tuning, the valid range is [0, 1.0], there is no default range, and the scale type is LINEAR.

Syntax

COLSAMPLE_BYNODE = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

The subsample ratio of columns for each node (split). Subsampling occurs once for every time a new split is evaluated. Columns are subsampled from the set of columns chosen for the current level.

Arguments

If you aren't running hyperparameter tuning, then you can specify a FLOAT64 value between 0 and 1.0. The default value is 1.0.

If you are running hyperparameter tuning, use one of the following options:

• The HPARAM_RANGE keyword and two FLOAT64 values that define the range of the hyperparameter. For example, COLSAMPLE_BYNODE = HPARAM_RANGE(0, 0.3)).
• The HPARAM_CANDIDATES keyword and an array of FLOAT64 values that provide discrete values to use for the hyperparameter. For example, COLSAMPLE_BYNODE = HPARAM_CANDIDATES([0, 0.1, 0.3, 0.5]).

When running hyperparameter tuning, the valid range is [0, 1.0], there is no default range, and the scale type is LINEAR.

Syntax

MIN_SPLIT_LOSS = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

The minimum loss reduction required to make a further partition on a leaf node of the tree. The larger the MIN_SPLIT_LOSS value is, the more conservative the algorithm is.

Arguments

If you aren't running hyperparameter tuning, then you can specify a FLOAT64 value. The default value is 0.

If you are running hyperparameter tuning, use one of the following options:

• The HPARAM_RANGE keyword and two FLOAT64 values that define the range of the hyperparameter. For example, MIN_SPLIT_LOSS = HPARAM_RANGE(0, 5.5).
• The HPARAM_CANDIDATES keyword and an array of FLOAT64 values that provide discrete values to use for the hyperparameter. For example, MIN_SPLIT_LOSS = HPARAM_CANDIDATES([0, 0.5, 1.5, 2.5]).

When running hyperparameter tuning, the valid range is [0, ∞), there is no default range, and the scale type is LINEAR.

Syntax

SUBSAMPLE = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) }

Description

The subsample ratio of the training instances. Setting this value to 0.5 means that training randomly samples half of the training data prior to growing trees, which prevents overfitting. Subsampling occurs once in every boosting iteration. This is independent of the training-test data split used in the training options. The test data is not used in any iteration irrespective of the SUBSAMPLE value; subsampling is only applied to the training data.

Arguments

If you aren't running hyperparameter tuning, then you can specify a FLOAT64 value between 0 and 1.0. The default value is 1.0, which specifies that subsampling uses all of the training data in each iteration.

If you are running hyperparameter tuning, use one of the following options:

• The HPARAM_RANGE keyword and two FLOAT64 values that define the range of the hyperparameter. For example, SUBSAMPLE = HPARAM_RANGE(0, 0.6).
• The HPARAM_CANDIDATES keyword and an array of FLOAT64 values that provide discrete values to use for the hyperparameter. For example, SUBSAMPLE = HPARAM_CANDIDATES([0, 0.1, 0.2, 0.6]).

When running hyperparameter tuning, the valid range is (0, 1.0], the default range is (0, 1.0], and the scale type is LINEAR.

Syntax

INSTANCE_WEIGHT_COL = string_value

Description

The column used to specify the weights for each data point in the training dataset. The column you specify must be a numerical column. You can't use this column as a feature or label, and it is excluded from features automatically. You can't specify this option if AUTO_CLASS_WEIGHTS is TRUE or if CLASS_WEIGHTS is set.

The INSTANCE_WEIGHT_COL option is only supported for non-array features.

Arguments

A STRING value.

Syntax

XGBOOST_VERSION = { '0.9' | '1.1' }

Description

The XGBoost version for model training.

Arguments

This option accepts the following values:

• 0.9. This is the default.
• 1.1

Syntax

AUTO_CLASS_WEIGHTS = { TRUE | FALSE }

Description

Determines whether to balance class labels by using weights for each class in inverse proportion to the frequency of that class.

Only use this option with classifier models.

By default, the training data used to create the model is unweighted. If the labels in the training data are imbalanced, the model might learn to predict the most popular class of labels more heavily, which you might not want.

To balance every class, set this option to TRUE. Balance is accomplished using the following formula:

total_input_rows / (input_rows_for_class_n * number_of_unique_classes)

Arguments

A BOOL value. The default value is FALSE.

Syntax

CLASS_WEIGHTS = struct_array

Description

The weights to use for each class label. You can't specify this option if AUTO_CLASS_WEIGHTS is TRUE.

Arguments

An ARRAY of STRUCT values. Each STRUCT contains a STRING value that specifies the class label and a FLOAT64 value that specifies the weight for that class label. A weight must be present for every class label. The weights are not required to add up to 1.

A CLASS_WEIGHTS value might look like the following example:

CLASS_WEIGHTS = [STRUCT('example_label', .2)]

Syntax

ENABLE_GLOBAL_EXPLAIN = { TRUE | FALSE }

Description

Determines whether to compute global explanations by using explainable AI to evaluate the importance of global features to the model.

Global explanations are computed when you create the model. This option must be TRUE if you want to use the ML.GLOBAL_EXPLAIN function to retrieve the global explanations after the model is created.

Arguments

A BOOL value. The default value is FALSE.

Syntax

EARLY_STOP = { TRUE | FALSE }

Description

Determines whether training should stop after the first iteration in which the relative loss improvement is less than the value specified for MIN_REL_PROGRESS.

Arguments

A BOOL value. The default value is TRUE.

Syntax

MIN_REL_PROGRESS = float64_value

Description

The minimum relative loss improvement that is necessary to continue training when EARLY_STOP is set to TRUE. For example, a value of 0.01 specifies that each iteration must reduce the loss by 1% for training to continue.

Arguments

A FLOAT64 value. The default value is 0.01.

Syntax

INPUT_LABEL_COLS = string_array

Description

The name of the label column in the training data. The label column contains the expected machine learning result for the given record. For example, in a spam detection dataset, the label column value would probably be either spam or not spam. In a rainfall dataset, the label column value might be the amount of rain that fell during a certain period.

Arguments

A one-element ARRAY of string values. Defaults to label.

Syntax

MAX_ITERATIONS = int64_value

Description

The maximum number of rounds for boosting.

Arguments

An INT64 value. The default value is 20.

Syntax

DATA_SPLIT_METHOD = { 'AUTO_SPLIT' | 'RANDOM' | 'CUSTOM' | 'SEQ' | 'NO_SPLIT' }

Description

The method used to split input data into training, evaluation, and, if you are running hyperparameter tuning, test data sets. Training data is used to train the model. Evaluation data is used to avoid overfitting by using early stopping. Test data is used to test the hyperparameter tuning trial and record its metrics in the model.

The percentage sizes of the data sets produced by the various arguments for this option are approximate. Larger input data sets come closer to the percentages described than smaller input data sets do.

You can see the model's data split information in the following ways:

• The data split method and percentage are shown in the Training Options section of the model's Details page on the BigQuery page of the Google Cloud console.
• Links to temporary tables that contain the split data are available in the Model Details section of the model's Details page on the BigQuery of the Google Cloud console. You can also return this information from the DataSplitResult field in the BigQuery API. These tables are saved for 48 hours. If you need this information for more than 48 hours, then you should export this data or copy it to permanent tables.

Arguments

This option accepts the following values:

*

: This is the default value. This option splits the data as follows:

• If there are fewer than 500 rows in the input data, then all rows are used as training data.

• If you aren't running hyperparameter tuning, then data is randomized and split as follows:

  • If there are between 500 and 50,000 rows in the input data, then 20% of the data is used as evaluation data and 80% is used as training data.
  • If there are more than 50,000 rows, then 10,000 rows are used as evaluation data and the remaining rows are used as training data.

• If you are running hyperparameter tuning and there are more than 500 rows in the input data, then the data is randomized and split as follows:

  • 10% of the data is used as evaluation data
  • 10% is used as test data

  • 80% is used as training data

    For more information, see Data split.

• RANDOM: Data is randomized before being split into sets. You can use this option with the DATA_SPLIT_EVAL_FRACTION and DATA_SPLIT_TEST_FRACTION options to customize the data split. If you don't specify either of those options, data is split in the same way as for the AUTO_SPLIT option.

  A random split is deterministic: different training runs produce the same split results if the same underlying training data is used.

  Note: A random split is based on the FARM_FINGERPRINT of the data (including the column name and schema), so tables with the same content but different column names and schemas may get different splitting and different evaluation metrics.

• CUSTOM: Split data using the value in a specified column:

  • If you aren't running hyperparameter tuning, then you must provide the name of a column of type BOOL. Rows with a value of TRUE or NULL are used as evaluation data, rows with a value of FALSE are used as training data.
  • If you are running hyperparameter tuning, then you must provide the name of a column of type STRING. Rows with a value of TRAIN are used as training data, rows with a value of EVAL are used as evaluation data, and rows with a value of TEST are used as test data.

  Use the DATA_SPLIT_COL option to identify the column that contains the data split information.

• SEQ: Split data sequentially by using the value in a specified column of one of the following types:

  • NUMERIC
  • BIGNUMERIC
  • STRING
  • TIMESTAMP

  The data is sorted smallest to largest based on the specified column.

  When you aren't running hyperparameter tuning, the first n rows are used as evaluation data, where n is the value specified for DATA_SPLIT_EVAL_FRACTION. The remaining rows are used as training data.

  When you are running hyperparameter tuning, the first n rows are used as evaluation data, where n is the value specified for DATA_SPLIT_EVAL_FRACTION. The next m rows are used as test data, where m is the value specified for DATA_SPLIT_TEST_FRACTION. The remaining rows are used as training data.

  All rows with split values smaller than the threshold are used as training data. The remaining rows, including NULLs, are used as evaluation data.

  Use the DATA_SPLIT_COL option to identify the column that contains the data split information.

• NO_SPLIT: No data split; all input data is used as training data.

Syntax

DATA_SPLIT_EVAL_FRACTION = float64_value

Description

The fraction of the data to use as evaluation data. Use when you are specifying RANDOM or SEQ as the value for the DATA_SPLIT_METHOD option.

If you are running hyperparameter tuning and you specify a value for this option, you must also specify a value for DATA_SPLIT_TEST_FRACTION. In this case, the training dataset is 1 - eval_fraction - test_fraction. For example, if you specify 20.00 for DATA_SPLIT_EVAL_FRACTION and 8.0 for DATA_SPLIT_TEST_FRACTION, your training dataset is 72% of the input data.

Arguments

A FLOAT64 value. The default is 0.2. The service maintains the accuracy of the input value to two decimal places.

Syntax

DATA_SPLIT_TEST_FRACTION = float64_value

Description

The fraction of the data to use as test data. Use this option when you are running hyperparameter tuning and specifying either RANDOM or SEQ as value for the DATA_SPLIT_METHOD option.

If you specify a value for this option, you must also specify a value for DATA_SPLIT_EVAL_FRACTION. In this case, the training dataset is 1 - eval_fraction - test_fraction. For example, if you specify 20.00 for DATA_SPLIT_EVAL_FRACTION and 8.0 for DATA_SPLIT_TEST_FRACTION, your training dataset is 72% of the input data.

Arguments

A FLOAT64 value. The default is 0. The service maintains the accuracy of the input value to two decimal places.

Syntax

DATA_SPLIT_COL = string_value

Description

The name of the column to use to sort input data into the training, evaluation, or test set. Use when you are specifying CUSTOM or SEQ as the value for the DATA_SPLIT_METHOD option:

• If you aren't running hyperparameter tuning and you are specifying SEQ as the value for DATA_SPLIT_METHOD, then the data is first sorted smallest to largest based on the specified column. The last n rows are used as evaluation data, where n is the value specified for DATA_SPLIT_EVAL_FRACTION. The remaining rows are used as training data.
• If you aren't running hyperparameter tuning and you are specifying CUSTOM as the value for DATA_SPLIT_METHOD, then you must provide the name of a column of type BOOL. Rows with a value of TRUE or NULLare used as evaluation data, rows with a value of FALSE are used as training data.
• If you are running hyperparameter tuning and you are specifying SEQ as the value for DATA_SPLIT_METHOD, then the data is first sorted smallest to largest based on the specified column. The last n rows are used as evaluation data, where n is the value specified for DATA_SPLIT_EVAL_FRACTION. The next m rows are used as test data, where m is the value specified for DATA_SPLIT_TEST_FRACTION. The remaining rows are used as training data.
• If you are running hyperparameter tuning and you are specifying CUSTOM as the value for DATA_SPLIT_METHOD, then you must provide the name of a column of type STRING. Rows with a value of TRAIN are used as training data, rows with a value of EVAL are used as evaluation data, and rows with a value of TEST are used as test data.

The column you specify for DATA_SPLIT_COL can't be used as a feature or label, and is excluded from features automatically.

Arguments

A STRING value.

Syntax

NUM_TRIALS = int64_value

Description

The maximum number of submodels to train. The tuning stops when NUM_TRIALS submodels are trained, or when the hyperparameter search space is exhausted. You must specify this option in order to use hyperparameter tuning.

Arguments

An INT64 value between 1 and 100, inclusive.

Syntax

MAX_PARALLEL_TRIALS = int64_value

Description

The maximum number of trials to run at the same time. If you specify a value for this option, you must also specify a value for NUM_TRIALS.

Arguments

An INT64 value between 1 and 5, inclusive. The default value is 1.

Syntax

HPARAM_TUNING_ALGORITHM = { 'VIZIER_DEFAULT' | 'RANDOM_SEARCH' | 'GRID_SEARCH' }

Description

The algorithm used to tune the hyperparameters. If you specify a value for this option, you must also specify a value for NUM_TRIALS.

Arguments

Specify one of the following values:

• VIZIER_DEFAULT: Use the default algorithm in Vertex AI Vizier to tune hyperparameters. This algorithm is the most powerful algorithm of those offered. It performs a mixture of advanced search algorithms, including Bayesian optimization with Gaussian processes. It also uses transfer learning to take advantage of previously tuned models. This is the default, and also the recommended approach.

• RANDOM_SEARCH: Use random search to explore the search space.

• GRID_SEARCH: Use grid search to explore the search space. You can only use this algorithm when every hyperparameter's search space is discrete.

Syntax

For BOOSTED_TREE_CLASSIFIER models:

HPARAM_TUNING_OBJECTIVES = { 'PRECISION' | 'RECALL' | 'ACCURACY' | 'F1_SCORE' | 'LOG_LOSS' | 'ROC_AUC' }

For BOOSTED_TREE_REGRESSOR models:

HPARAM_TUNING_OBJECTIVES = { 'MEAN_ABSOLUTE_ERROR' | 'MEAN_SQUARED_ERROR' | 'MEAN_SQUARED_LOG_ERROR' | 'MEDIAN_ABSOLUTE_ERROR' | 'R2_SCORE' | 'EXPLAINED_VARIANCE' }

Description The hyperparameter tuning objective for the model; only one objective is supported. If you specify a value for this option, you must also specify a value for NUM_TRIALS.

Arguments

The possible objectives are a subset of the model evaluation metrics for the model type. If you aren't running hyperparameter tuning, or if you are and you don't specify an objective, the default objective is used. For BOOSTED_TREE_CLASSIFIER models, the default is ROC_AUC. For BOOSTED_TREE_REGRESSOR models, the default is R2_SCORE.

Syntax

KMS_KEY_NAME = string_value

Description

The Cloud Key Management Service customer-managed encryption key (CMEK) to use to encrypt the model.

Arguments

A STRING value containing the fully-qualified name of the CMEK. For example,

'projects/my_project/locations/my_location/keyRings/my_ring/cryptoKeys/my_key'

BigQuery ML uses the following default values when building models:

loss_reduction = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE

batch_norm = False

Boosted tree classification and regression models support hyperparameter tuning, which you can use to improve model performance for your data. To use hyperparameter tuning, set the NUM_TRIALs option to the number of trials that you want to run. BigQuery ML then trains the model the number of times that you specify, using different hyperparameter values, and returns the model that performs the best.

Hyperparameter tuning defaults to improving the key performance metric for the given model type. You can use the HPARAM_TUNING_OBJECTIVES option to tune for a different metric if you need to.

For more information about the training objectives and hyperparameters supported for boosted tree classification models, see BOOSTED_TREE_CLASSIFIER. For more information about the training objectives and hyperparameters supported for boosted tree regression models, see BOOSTED_TREE_REGRESSOR. To try a tutorial that walks you through hyperparameter tuning, see Improve model performance with hyperparameter tuning.

For information about supported locations, see Locations for non-remote models.

CREATE MODEL statements must comply with the following rules:

• For BOOSTED_TREE_CLASSIFIER models, the label column can contain up to 1,000 unique values; that is, the number of classes is less than or equal to 1,000. If you need to classify into more than 1,000 labels, contact bqml-feedback@google.com.

The following example trains a boosted tree classifier model against 'mytable' with 'mylabel' as the label column.

CREATE MODEL `project_id.mydataset.mymodel`
OPTIONS(MODEL_TYPE='BOOSTED_TREE_CLASSIFIER',
        BOOSTER_TYPE = 'GBTREE',
        NUM_PARALLEL_TREE = 1,
        MAX_ITERATIONS = 50,
        TREE_METHOD = 'HIST',
        EARLY_STOP = FALSE,
        SUBSAMPLE = 0.85,
        INPUT_LABEL_COLS = ['mylabel'])
AS SELECT * FROM `project_id.mydataset.mytable`;

The following example creates and trains a boosted tree regression model. It uses hyperparameter tuning to improve model performance.

CREATE MODEL
  `mydataset.mymodel`
OPTIONS
  ( MODEL_TYPE='BOOSTED_TREE_REGRESSOR',
    num_trials=10,
    max_parallel_trials=2,
    HPARAM_TUNING_OBJECTIVES=['R2_SCORE'] ) AS
SELECT
  column1,
  column2,
  column3,
  label
FROM
  `mydataset.mytable`

Training boosted tree models is not supported in all BigQuery ML regions. For a complete list of supported regions and multi-regions, see BigQuery ML locations.

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