The CREATE MODEL statement

To create a model in BigQuery, use the BigQuery ML CREATE MODEL statement. This statement is similar to the CREATE TABLE DDL statement. When you run a query that contains a CREATE MODEL statement, a query job is generated for you that processes the query.

For information about supported model types of each SQL statement and function, and all supported SQL statements and functions for each model type, read End-to-end user journey for each model.

Note: This syntax statement provides a comprehensive list of model types with their model options. When creating a model, use that model specific CREATE MODEL statement for convenience. You can view specific CREATE MODEL statements by clicking the MODEL_TYPE name in list below, in the table of contents in the left panel, or in the create model link in the End-to-end user journey for each model.

CREATE MODEL syntax

{CREATE MODEL | CREATE MODEL IF NOT EXISTS | CREATE OR REPLACE MODEL}
model_name
[TRANSFORM (select_list)]
[INPUT (field_name field_type)
 OUTPUT (field_name field_type)]
[REMOTE WITH CONNECTION `connection_name`]
[OPTIONS(model_option_list)]
[AS {query_statement |
  (
    training_data AS (query_statement),
    custom_holiday AS (holiday_statement)
  )}]

model_option_list:
    MODEL_TYPE = { 'LINEAR_REG' |
                   'LOGISTIC_REG' |
                   'KMEANS' |
                   'MATRIX_FACTORIZATION' |
                   'PCA' |
                   'AUTOENCODER' |
                   'AUTOML_CLASSIFIER' |
                   'AUTOML_REGRESSOR' |
                   'BOOSTED_TREE_CLASSIFIER' |
                   'BOOSTED_TREE_REGRESSOR' |
                   'RANDOM_FOREST_CLASSIFIER' |
                   'RANDOM_FOREST_REGRESSOR' |
                   'DNN_CLASSIFIER' |
                   'DNN_REGRESSOR' |
                   'DNN_LINEAR_COMBINED_CLASSIFIER' |
                   'DNN_LINEAR_COMBINED_REGRESSOR' |
                   'ARIMA_PLUS' |
                   'ARIMA_PLUS_XREG' |
                   'TENSORFLOW' |
                   'TENSORFLOW_LITE' |
                   'ONNX' |
                   'XGBOOST'}
    [, MODEL_REGISTRY = { 'VERTEX_AI' } ]
    [, VERTEX_AI_MODEL_ID = string_value ]
    [, VERTEX_AI_MODEL_VERSION_ALIASES = string_array ]
    [, INPUT_LABEL_COLS = string_array ]
    [, MAX_ITERATIONS = int64_value ]
    [, EARLY_STOP = { TRUE | FALSE } ]
    [, MIN_REL_PROGRESS = float64_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 ]
    [, OPTIMIZE_STRATEGY = { 'AUTO_STRATEGY' | 'BATCH_GRADIENT_DESCENT' | 'NORMAL_EQUATION' } ]
    [, L1_REG = float64_value ]
    [, L2_REG = float64_value ]
    [, LEARN_RATE_STRATEGY = { 'LINE_SEARCH' | 'CONSTANT' } ]
    [, LEARN_RATE = float64_value ]
    [, LS_INIT_LEARN_RATE = float64_value ]
    [, WARM_START = { TRUE | FALSE } ]
    [, AUTO_CLASS_WEIGHTS = { TRUE | FALSE } ]
    [, CLASS_WEIGHTS = struct_array ]
    [, INSTANCE_WEIGHT_COL = string_value ]
    [, NUM_CLUSTERS = int64_value ]
    [, KMEANS_INIT_METHOD = { 'RANDOM' | 'KMEANS++' | 'CUSTOM' } ]
    [, KMEANS_INIT_COL = string_value ]
    [, DISTANCE_TYPE = { 'EUCLIDEAN' | 'COSINE' } ]
    [, STANDARDIZE_FEATURES = { TRUE | FALSE } ]
    [, MODEL_PATH = string_value ]
    [, BUDGET_HOURS = float64_value ]
    [, OPTIMIZATION_OBJECTIVE = { string_value | struct_value } ]
    [, FEEDBACK_TYPE = {'EXPLICIT' | 'IMPLICIT'} ]
    [, NUM_FACTORS = int64_value ]
    [, USER_COL = string_value ]
    [, ITEM_COL = string_value ]
    [, RATING_COL = string_value ]
    [, WALS_ALPHA = float64_value ]
    [, BOOSTER_TYPE = { 'gbtree' | 'dart'} ]
    [, NUM_PARALLEL_TREE = int64_value ]
    [, DART_NORMALIZE_TYPE = { 'tree' | 'forest'} ]
    [, TREE_METHOD = { 'auto' | 'exact' | 'approx' | 'hist'} ]
    [, MIN_TREE_CHILD_WEIGHT = float64_value ]
    [, COLSAMPLE_BYTREE = float64_value ]
    [, COLSAMPLE_BYLEVEL = float64_value ]
    [, COLSAMPLE_BYNODE = float64_value ]
    [, MIN_SPLIT_LOSS = float64_value ]
    [, MAX_TREE_DEPTH = int64_value ]
    [, SUBSAMPLE = float64_value ]
    [, ACTIVATION_FN = { 'RELU' | 'RELU6' | 'CRELU' | 'ELU' | 'SELU' | 'SIGMOID' | 'TANH' } ]
    [, BATCH_SIZE = int64_value ]
    [, DROPOUT = float64_value ]
    [, HIDDEN_UNITS = int_array ]
    [, OPTIMIZER = { 'ADAGRAD' | 'ADAM' | 'FTRL' | 'RMSPROP' | 'SGD' } ]
    [, TIME_SERIES_TIMESTAMP_COL = string_value ]
    [, TIME_SERIES_DATA_COL = string_value ]
    [, TIME_SERIES_ID_COL = { string_value | string_array } ]
    [, HORIZON = int64_value ]
    [, AUTO_ARIMA = { TRUE | FALSE } ]
    [, AUTO_ARIMA_MAX_ORDER = int64_value ]
    [, AUTO_ARIMA_MIN_ORDER = int64_value ]
    [, NON_SEASONAL_ORDER = (int64_value, int64_value, int64_value) ]
    [, DATA_FREQUENCY = { 'AUTO_FREQUENCY' | 'PER_MINUTE' | 'HOURLY' | 'DAILY' | 'WEEKLY' | ... } ]
    [, FORECAST_LIMIT_LOWER_BOUND = float64_value  ]
    [, FORECAST_LIMIT_UPPER_BOUND = float64_value  ]
    [, INCLUDE_DRIFT = { TRUE | FALSE } ]
    [, HOLIDAY_REGION = { 'GLOBAL' | 'NA' | 'JAPAC' | 'EMEA' | 'LAC' | 'AE' | ... } ]
    [, CLEAN_SPIKES_AND_DIPS = { TRUE | FALSE } ]
    [, ADJUST_STEP_CHANGES = { TRUE | FALSE } ]
    [, DECOMPOSE_TIME_SERIES = { TRUE | FALSE } ]
    [, HIERARCHICAL_TIME_SERIES_COLS = { string_array } ]
    [, ENABLE_GLOBAL_EXPLAIN = { TRUE | FALSE } ]
    [, APPROX_GLOBAL_FEATURE_CONTRIB = { TRUE | FALSE }]
    [, INTEGRATED_GRADIENTS_NUM_STEPS = int64_value ]
    [, CALCULATE_P_VALUES = { TRUE | FALSE } ]
    [, FIT_INTERCEPT = { TRUE | FALSE } ]
    [, CATEGORY_ENCODING_METHOD = { 'ONE_HOT_ENCODING' | 'DUMMY_ENCODING' | 'LABEL_ENCODING' | 'TARGET_ENCODING' } ]
    [, ENDPOINT = string_value ]
    [, REMOTE_SERVICE_TYPE = { 'CLOUD_AI_VISION_V1' | 'CLOUD_AI_NATURAL_LANGUAGE_V1' | 'CLOUD_AI_TRANSLATE_V3' } ]
    [, XGBOOST_VERSION = { '0.9' | '1.1' } ]
    [, TF_VERSION = { '1.15' | '2.8.0' } ]
    [, NUM_TRIALS = int64_value, ]
    [, MAX_PARALLEL_TRIALS = int64_value ]
    [, HPARAM_TUNING_ALGORITHM = { 'VIZIER_DEFAULT' | 'RANDOM_SEARCH' | 'GRID_SEARCH' } ]
    [, HPARAM_TUNING_OBJECTIVES = { 'R2_SCORE' | 'ROC_AUC' | ... } ]
    [, NUM_PRINCIPAL_COMPONENTS = int64_value ]
    [, PCA_EXPLAINED_VARIANCE_RATIO = float64_value ]
    [, SCALE_FEATURES = { TRUE | FALSE } ]
    [, PCA_SOLVER = { 'FULL' | 'RANDOMIZED' | 'AUTO' } ]
    [, TIME_SERIES_LENGTH_FRACTION = float64_value ]
    [, MIN_TIME_SERIES_LENGTH = int64_value ]
    [, MAX_TIME_SERIES_LENGTH = int64_value ]
    [, TREND_SMOOTHING_WINDOW_SIZE = int64_value ]
    [, SEASONALITIES = string_array ]
    [, PROMPT_COL = string_value ]
    [, LEARNING_RATE_MULTIPLIER = float64_value ]
    [, ACCELERATOR_TYPE = { 'GPU' | 'TPU' } ]
    [, EVALUATION_TASK = { 'TEXT_GENERATION' | 'CLASSIFICATION' | 'SUMMARIZATION' | 'QUESTION_ANSWERING' | 'UNSPECIFIED' } ]
    [, DOCUMENT_PROCESSOR = string_value ]
    [, SPEECH_RECOGNIZER = string_value ]
    [, KMS_KEY_NAME = string_value ]
    [, CONTRIBUTION_METRIC = string_value ]
    [, DIMENSION_ID_COLS = string_array ]
    [, IS_TEST_COL = string_value ]
    [, MIN_APRIORI_SUPPORT = float64_value ]

CREATE MODEL

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

CREATE MODEL IF NOT EXISTS

Creates and trains a new model only if the model does not exist in the specified dataset.

CREATE OR REPLACE MODEL

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

model_name

model_name is the name of the model you're creating or replacing. The model name must be unique per 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, prepend the project ID to the model name in following format, including backticks: `[PROJECT_ID].[DATASET].[MODEL]`; for example, `myproject.mydataset.mymodel`.

TRANSFORM

TRANSFORM lets you specify all preprocessing during model creation and have it automatically applied during prediction and evaluation.

For example, you can create the following model:

CREATE OR REPLACE MODEL `myproject.mydataset.mymodel`
  TRANSFORM(ML.FEATURE_CROSS(STRUCT(f1, f2)) as cross_f,
            ML.QUANTILE_BUCKETIZE(f3) OVER() as buckets,
            label_col)
  OPTIONS(model_type='linear_reg', input_label_cols=['label_col'])
AS SELECT * FROM t

During prediction, you don't need to preprocess the input again, and the same transformations are automatically restored:

SELECT * FROM ML.PREDICT(MODEL `myproject.mydataset.mymodel`, (SELECT f1, f2, f3 FROM table))

When the TRANSFORM clause is present, only output columns from the TRANSFORM clause are used in training. Any results from query_statement that don't appear in the TRANSFORM clause are ignored.

The input columns of the TRANSFORM clause are the result of query_statement. So, the final input used in training is the set of columns generated by the following query:

SELECT (select_list) FROM (query_statement);

Input columns of the TRANSFORM clause can be of any SIMPLE type or ARRAY of SIMPLE type. SIMPLE types are non-STRUCT and non-ARRAY data types.

In prediction (ML.PREDICT), users only need to pass in the original columns from the query_statement that are used inside the TRANSFORM clause. The columns dropped in TRANSFORM don't need to be provided during prediction. TRANSFORM is automatically applied to the input data during prediction, including the statistics used in ML analytic functions (for example, ML.QUANTILE_BUCKETIZE).

To learn more about feature preprocessing, see Feature preprocessing overview, or try the Feature Engineering Functions notebook.

To try using the TRANSFORM clause, try the Use the BigQuery ML TRANSFORM clause for feature engineering tutorial or the Create Model With Inline Transpose notebook.

select_list

You can pass columns from query_statement through to model training without transformation by either using *, * EXCEPT(), or by listing the column names directly.

Not all columns from query_statement are required to appear in the TRANSFORM clause, so you can drop columns appearing in query_statement by omitting them from the TRANSFORM clause.

You can transform inputs from query_statement by using expressions in select_list. select_list is similar to a normal SELECT statement. select_list supports the following syntax:

• *
• * EXCEPT()
• * REPLACE()
• expression
• expression.*

The following cannot appear inside select_list:

• Aggregation functions.
• Non-BigQuery ML analytic functions. For more information about supported functions, see Manual feature preprocessing.
• UDFs.
• Subqueries.
• Anonymous columns. For example, a + b as c is allowed, while a + b isn't.

The output columns of select_list can be of any BigQuery supported data type.

If present, the following columns must appear in select_list without transformation:

• label
• data_split_col
• kmeans_init_col
• instance_weight_col

If these columns are returned by query_statement, you must reference them in select_list by column name outside of any expression, or by using *. You can't use aliases with these columns.

INPUT and OUTPUT

INPUT and OUTPUT clauses are used to specify input and output format for remote models or XGBoost models.

field_name

For remote models, INPUT and OUTPUT field names must be identical as the field names of the Vertex AI endpoint request and response. See examples in remote model INPUT and OUTPUT clause.

For XGBoost models, INPUT field names must be identical to the names in the feature_names field if feature_names field is populated in the XGBoost model file. See XGBoost INPUT OUTPUT clause for more details.

field_type

Remote models support the following BigQuery data types for INPUT and OUTPUT clauses:

• Simple type: BOOL, INT64, FLOAT64, NUMERIC, BIGNUMERIC, STRING
• ARRAY<Simple type>

XGBoost models support the following BigQuery data types for INPUT field type:

• Numeric type
• ARRAY<Numeric type>

XGBoost models only support FLOAT64 for OUTPUT field type.

connection_name

BigQuery uses a CLOUD_RESOURCE connection to interact with your Vertex AI endpoint. You need to grant Vertex AI User role to connection's service account on your Vertex AI endpoint project.

See examples in remote model CONNECTION statement

model_option_list

CREATE MODEL supports the following options:

MODEL_TYPE

Syntax

MODEL_TYPE = { 'LINEAR_REG' | 'LOGISTIC_REG' | 'KMEANS' | 'PCA' |
'MATRIX_FACTORIZATION' | 'AUTOENCODER' | 'AUTOML_REGRESSOR' |
'AUTOML_CLASSIFIER' | 'BOOSTED_TREE_CLASSIFIER' | 'BOOSTED_TREE_REGRESSOR' |
'RANDOM_FOREST_CLASSIFIER' | 'RANDOM_FOREST_REGRESSOR' |
'DNN_CLASSIFIER' | 'DNN_REGRESSOR' | 'DNN_LINEAR_COMBINED_CLASSIFIER' |
'DNN_LINEAR_COMBINED_REGRESSOR' | 'ARIMA_PLUS' | 'ARIMA_PLUS_XREG' |
'TENSORFLOW' | 'TENSORFLOW_LITE' | 'ONNX' | 'XGBOOST'}

Description

Specify the model type. This argument is required.

Arguments

The argument is in the model type column.

Model category	Model type	Description	Model specific CREATE MODEL statement
Regression	'LINEAR_REG'	Linear regression for real-valued label prediction; for example, the sales of an item on a given day.	CREATE MODEL statement for generalized linear models
	'BOOSTED_TREE_REGRESSOR'	Create a boosted tree regressor model using the XGBoost library.	CREATE MODEL statement for boosted tree models
	'RANDOM_FOREST_REGRESSOR'	Create a random forest regressor model using the XGBoost library.	CREATE MODEL statement for random forest models
	'DNN_REGRESSOR'	Create a Deep Neural Network Regressor model.	CREATE MODEL statement for DNN models
	'DNN_LINEAR_COMBINED_REGRESSOR'	Create a Wide-and-Deep Regressor model.	CREATE MODEL statement for Wide-and-Deep models
	'AUTOML_REGRESSOR'	Create a regression model using AutoML.	CREATE MODEL statement for AutoML models
Classification	'LOGISTIC_REG'	Logistic regression for binary-class or multi-class classification; for example, determining whether a customer will make a purchase.	CREATE MODEL statement for generalized linear models
	'BOOSTED_TREE_CLASSIFIER'	Create a boosted tree classifier model using the XGBoost library.	CREATE MODEL statement for boosted tree models
	'RANDOM_FOREST_CLASSIFIER'	Create a random forest classifier model using the XGBoost library.	CREATE MODEL statement for random forest models
	'DNN_CLASSIFIER'	Create a Deep Neural Network Classifier model.	CREATE MODEL statement for DNN models
	'DNN_LINEAR_COMBINED_CLASSIFIER'	Create a Wide-and-Deep Classifier model.	CREATE MODEL statement for Wide-and-Deep models
	'AUTOML_CLASSIFIER'	Create a classification model using AutoML.	CREATE MODEL statement for AutoML models
Clustering	'KMEANS'	K-means clustering for data segmentation; for example, identifying customer segments.	CREATE MODEL statement for K-means models
Collaborative Filtering	'MATRIX_FACTORIZATION'	Matrix factorization for recommendation systems. For example, given a set of users, items, and some ratings for a subset of the items, creates a model to predict a user's rating for items they have not rated.	CREATE MODEL statement for matrix factorization models
Dimensionality Reduction	'PCA'	Principal component analysis for dimensionality reduction.	CREATE MODEL statement for PCA models
	'AUTOENCODER'	Create an Autoencoder model for anomaly detection, dimensionality reduction, and embedding purposes.	CREATE MODEL statement for Autoencoder model
Time series forecasting	'ARIMA_PLUS' (previously 'ARIMA')	Univariate time-series forecasting with many modeling components under the hood such as ARIMA model for the trend, STL and ETS for seasonality, holiday effects, and so on.	CREATE MODEL statement for time series models
	'ARIMA_PLUS_XREG'	Multivariate time-series forecasting using linear regression and ARIMA_PLUS as the underlying techniques.	CREATE MODEL statement for time series models
Importing models	'TENSORFLOW'	Create a model by importing a TensorFlow model into BigQuery.	CREATE MODEL statement for TensorFlow models
	'TENSORFLOW_LITE'	Create a model by importing a TensorFlow Lite model into BigQuery.	CREATE MODEL statement for TensorFlow Lite models
	'ONNX'	Create a model by importing an ONNX model into BigQuery.	CREATE MODEL statement for ONNX models
	'XGBOOST'	Create a model by importing a XGBoost model into BigQuery.	CREATE MODEL statement for XGBoost models
Remote models	NA	Create a model by specifying a Cloud AI service, or the endpoint for a Vertex AI model.	CREATE MODEL statement for remote models over Google models in Vertex AI CREATE MODEL statement for remote models over hosted models in Vertex AI CREATE MODEL statement for remote models over Cloud AI services

Note: We are deprecating ARIMA as the model type. While the model training pipelines of ARIMA and ARIMA_PLUS are the same, ARIMA_PLUS supports more functionality, including support for a new training option, DECOMPOSE_TIME_SERIES, and table-valued functions including ML.ARIMA_EVALUATE and ML.EXPLAIN_FORECAST.

Other model options

The table below provides a comprehensive list of model options, with a brief description and their applicable model types. You can find detailed description in the model specific CREATE MODEL statement by clicking the model type in the "Applied model types" column.

When the applied model types are supervised learning models, unless "regressor" or "classifier" is explicitly listed, it means that model options apply to both the regressor and the classifier. For example, the "boosted tree" means that model option applies to both boosted tree regressor and boosted tree classifier, while the "boosted tree classifier" only applies to the classifier.

Name	Description	Applied model types
MODEL_REGISTRY	The MODEL_REGISTRY option specifies the model registry destination. `VERTEX_AI` is the only supported model registry destination. To learn more, see MLOps with BigQuery ML and Vertex AI.	All model types are supported.
VERTEX_AI_MODEL_ID	The Vertex AI model ID to register the model with. You can only set the VERTEX_AI_MODEL_ID option when the MODEL_REGISTRY option is set to 'VERTEX_AI'. To learn more, see Add a Vertex AI model ID.	All model types are supported.
VERTEX_AI_MODEL_VERSION_ALIASES	The Vertex AI model alias to register the model with. You can only set the VERTEX_AI_MODEL_VERSION_ALIASES option when the MODEL_REGISTRY option is set to 'VERTEX_AI'. To learn more, see Add a Vertex AI model alias.	All model types are supported.
INPUT_LABEL_COLS	The label column names in the training data.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep, AutoML
MAX_ITERATIONS	The maximum number of training iterations or steps.	Linear & logistic regression, Boosted trees, DNN, Wide & Deep, Kmeans, Matrix factorization, Autoencoder
EARLY_STOP	Whether training should stop after the first iteration in which the relative loss improvement is less than the value specified for `MIN_REL_PROGRESS`.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep, Kmeans, Matrix factorization, Autoencoder
MIN_REL_PROGRESS	The minimum relative loss improvement that is necessary to continue training when `EARLY_STOP` is set to true.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep, Kmeans, Matrix factorization, Autoencoder
DATA_SPLIT_METHOD	The method to split input data into training and evaluation sets when not running hyperparameter tuning, or into training, evaluation, and test sets when running hyperparameter tuning.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep Matrix factorization
DATA_SPLIT_EVAL_FRACTION	Specifies the fraction of the data used for evaluation. Accurate to two decimal places.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep Matrix factorization
DATA_SPLIT_TEST_FRACTION	Specifies the fraction of the data used for testing when you are running hyperparameter tuning. Accurate to two decimal places.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep Matrix factorization
DATA_SPLIT_COL	Identifies the column used to split the data.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep Matrix factorization
OPTIMIZE_STRATEGY	The strategy to train linear regression models.	Linear regression
L1_REG	The amount of L1 regularization applied.	Linear & logistic regression, Boosted trees Random forest
L2_REG	The amount of L2 regularization applied.	Linear & logistic regression, Boosted trees, Random forest, Matrix factorization, ARIMA_PLUS_XREG
LEARN_RATE_STRATEGY	The strategy for specifying the learning rate during training.	Linear & logistic regression
LEARN_RATE	The learn rate for gradient descent when LEARN_RATE_STRATEGY is set to CONSTANT.	Linear & logistic regression
LS_INIT_LEARN_RATE	Sets the initial learning rate that LEARN_RATE_STRATEGY=LINE_SEARCH uses.	Linear & logistic regression
WARM_START	Retrain a model with new training data, new model options, or both.	Linear & logistic regression, DNN, Wide & Deep, Kmeans, Autoencoder
AUTO_CLASS_WEIGHTS	Whether to balance class labels using weights for each class in inverse proportion to the frequency of that class.	Logistic regression, Boosted tree classifier, Random forest classifier, DNN classifier, Wide & Deep classifier
CLASS_WEIGHTS	The weights to use for each class label. This option cannot be specified if AUTO_CLASS_WEIGHTS is specified. It takes an ARRAY of STRUCTs; each STRUCT is a (STRING, FLOAT64) pair representing a class label and the corresponding weight. A weight must be present for every class label. The weights are not required to add up to one. For example: CLASS_WEIGHTS = [STRUCT('example_label', .2)].	Logistic regression, Boosted tree classifier, Random forest classifier, DNN classifier, Wide & Deep classifier
INSTANCE_WEIGHT_COL	Identifies the column used to specify the weights for each data point in the training dataset.	Boosted trees, Random forest
NUM_CLUSTERS	The number of clusters to identify in the input data.	Kmeans
KMEANS_INIT_METHOD	The method of initializing the clusters.	Kmeans
KMEANS_INIT_COL	Identifies the column used to initialize the centroids.	Kmeans
DISTANCE_TYPE	The type of metric to compute the distance between two points.	K-means
STANDARDIZE_FEATURES	Whether to standardize numerical features.	Kmeans
BUDGET_HOURS	Sets the training budget hours.	AutoML
OPTIMIZATION_OBJECTIVE	Sets the optimization objective function to use for AutoML.	AutoML
MODEL_PATH	Specifies the location of the imported model to import.	Imported TensorFlow model, Imported TensorFlow lite model, Imported ONNX model, Imported XGBoost model
FEEDBACK_TYPE	Specifies feedback type for matrix factorization models which changes the algorithm that is used during training.	Matrix factorization
NUM_FACTORS	Specifies the number of latent factors.	Matrix factorization
USER_COL	The user column name.	Matrix factorization
ITEM_COL	The item column name.	Matrix factorization
RATING_COL	The rating column name.	Matrix factorization
WALS_ALPHA	A hyperparameter for matrix factorization models with IMPLICIT feedback.	Matrix factorization
BOOSTER_TYPE	For boosted tree models, specify the booster type to use, with default value GBTREE.	Boosted trees
NUM_PARALLEL_TREE	Number of parallel trees constructed during each iteration.	Boosted trees, Random forest
DART_NORMALIZE_TYPE	Type of normalization algorithm for DART booster.	Boosted trees
TREE_METHOD	Type of tree construction algorithm.	Boosted trees, Random forest
MIN_TREE_CHILD_WEIGHT	Minimum sum of instance weight needed in a child for further partitioning.	Boosted trees, Random forest
COLSAMPLE_BYTREE	Subsample ratio of columns when constructing each tree. Subsampling occurs once for every tree constructed.	Boosted trees, Random forest
COLSAMPLE_BYLEVEL	Subsample ratio of columns for each level. Subsampling occurs once for every new depth level reached in a tree.	Boosted trees, Random forest
COLSAMPLE_BYNODE	Subsample ratio of columns for each node (split). Subsampling occurs once every time a new split is evaluated.	Boosted trees, Random forest
MIN_SPLIT_LOSS	Minimum loss reduction required to make a further partition on a leaf node of the tree.	Boosted trees, Random forest
MAX_TREE_DEPTH	Maximum depth of a tree.	Boosted trees, Random forest
SUBSAMPLE	Subsample ratio of the training instances.	Boosted trees, Random forest
ACTIVATION_FN	Specifies the activation function of the neural network.	DNN, Wide & Deep, Autoencoder
BATCH_SIZE	Specifies the mini batch size of samples that are fed to the neural network.	DNN, Wide & Deep, Autoencoder
DROPOUT	Specifies the dropout rate of units in the neural network.	DNN, Wide & Deep, Autoencoder
HIDDEN_UNITS	Specifies the hidden layers of the neural network.	DNN, Wide & Deep, Autoencoder
OPTIMIZER	Specifies the optimizer for training the model.	DNN, Wide & Deep, Autoencoder
TIME_SERIES_TIMESTAMP_COL	The timestamp column name for time series models.	ARIMA_PLUS, ARIMA_PLUS_XREG
TIME_SERIES_DATA_COL	The data column name for time series models.	ARIMA_PLUS, ARIMA_PLUS_XREG
TIME_SERIES_ID_COL	The ID column names for time-series models.	ARIMA_PLUS
HORIZON	The number of time points to forecast. When forecasting multiple time series at once, this parameter applies to each time series.	ARIMA_PLUS, ARIMA_PLUS_XREG
AUTO_ARIMA	Whether the training process should use auto.ARIMA or not.	ARIMA_PLUS, ARIMA_PLUS_XREG
AUTO_ARIMA_MAX_ORDER	The maximum value for the sum of non-sesonal p and q. It controls the parameter search space in the auto.ARIMA algorithm.	ARIMA_PLUS, ARIMA_PLUS_XREG
AUTO_ARIMA_MIN_ORDER	The minimum value for the sum of non-sesonal p and q. It controls the parameter search space in the auto.ARIMA algorithm.	ARIMA_PLUS, ARIMA_PLUS_XREG
NON_SEASONAL_ORDER	The tuple of non-seasonal p, d, and q for the ARIMA_PLUS model.	ARIMA_PLUS, ARIMA_PLUS_XREG
DATA_FREQUENCY	The data frequency of the input time series.	ARIMA_PLUS, ARIMA_PLUS_XREG
FORECAST_LIMIT_LOWER_BOUND	The lower bound of the time series forecasting values.	ARIMA_PLUS
FORECAST_LIMIT_UPPER_BOUND	The upper bound of the time series forecasting values.	ARIMA_PLUS
INCLUDE_DRIFT	Should the ARIMA_PLUS model include a linear drift term or not.	ARIMA_PLUS, ARIMA_PLUS_XREG
HOLIDAY_REGION	The geographical region based on which the holiday effect is applied in modeling.	ARIMA_PLUS, ARIMA_PLUS_XREG
CLEAN_SPIKES_AND_DIPS	Whether the spikes and dips should be cleaned.	ARIMA_PLUS, ARIMA_PLUS_XREG
ADJUST_STEP_CHANGES	Whether the step changes should be adjusted.	ARIMA_PLUS, ARIMA_PLUS_XREG
DECOMPOSE_TIME_SERIES	Whether the separate components of both the history and the forecast parts of the time series (such as seasonal components) should be saved.	ARIMA_PLUS
HIERARCHICAL_TIME_SERIES_COLS	The column names used to generate hierarchical time series forecasts. The column order represents the hierarchy structure.	ARIMA_PLUS
ENABLE_GLOBAL_EXPLAIN	Specifies whether to compute global explanations using explainable AI to evaluate global feature importance to the model.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep
APPROX_GLOBAL_FEATURE_CONTRIB	Specifies whether to use fast approximation for feature contribution computation.	Boosted trees, Random forest
INTEGRATED_GRADIENTS_NUM_STEPS	Specifies the number of steps to sample between the example being explained and its baseline for approximating the integral in integrated gradients attribution methods.	DNN, Wide & Deep
CALCULATE_P_VALUES	Specifies whether to compute p-values for the model during training.	Linear & logistic regression
FIT_INTERCEPT	Specifies whether to fit an intercept for the model during training.	Linear & logistic regression
CATEGORY_ENCODING_METHOD	Specifies the default encoding method for categorical features.	Linear & logistic regression, Boosted trees
ENDPOINT	Specifies the Vertex AI endpoint to use for a remote model. This can be the name of a Google model in Vertex AI or the HTTPS endpoint of a model deployed to Vertex AI.	Remote models over Google models in Vertex AI Remote models over hosted models in Vertex AI
REMOTE_SERVICE_TYPE	Specifies the Cloud AI service to use for a remote model.	Remote models over Cloud AI services
XGBOOST_VERSION	Specifies the Xgboost version for model training.	Boosted trees, Random forest
TF_VERSION	Specifies the Tensorflow (TF) version for model training.	DNN, Wide & Deep, Autoencoder
NUM_TRIALS	Specifies the maximum number of submodels to train when you are running hyperparameter tuning.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep, Kmeans, Matrix factorization, Autoencoder
MAX_PARALLEL_TRIALS	Specifies the maximum number of trials to run at the same time when you are running hyperparameter tuning.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep, Kmeans, Matrix factorization, Autoencoder
HPARAM_TUNING_ALGORITHM	Specifies the algorithm used to tune the hyperparameters when you are running hyperparameter tuning.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep, Kmeans, Matrix factorization, Autoencoder
HPARAM_TUNING_OBJECTIVES	Specifies the hyperparameter tuning objective for the model.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep, Kmeans, Matrix factorization, Autoencoder
NUM_PRINCIPAL_COMPONENTS	The number of principal components to keep.	PCA
PCA_EXPLAINED_VARIANCE_RATIO	The ratio for the explained variance.	PCA
SCALE_FEATURES	Determines whether or not to scale the numerical features to unit variance.	PCA
PCA_SOLVER	The solver to use to calculate the principal components.	PCA
TIME_SERIES_LENGTH_FRACTION	The fraction of the interpolated length of the time series that's used to model the time series trend component.	ARIMA_PLUS, ARIMA_PLUS_XREG
MIN_TIME_SERIES_LENGTH	The minimum number of time points that are used in modeling the trend component of the time series.	ARIMA_PLUS, ARIMA_PLUS_XREG
MAX_TIME_SERIES_LENGTH	The maximum number of time points that are used in modeling the trend component of the time series.	ARIMA_PLUS, ARIMA_PLUS_XREG
TREND_SMOOTHING_WINDOW_SIZE	The smoothing window size for the trend component.	ARIMA_PLUS, ARIMA_PLUS_XREG
SEASONALITIES	The seasonality of the time series data refers to the presence of variations that occur at certain regular intervals such as weekly, monthly or quarterly.	ARIMA_PLUS
PROMPT_COL	The name of the prompt column in the training data table to use when performing supervised tuning.	Remote models over Google models in Vertex AI
LEARNING_RATE_MULTIPLIER	A multiplier to apply to the recommended learning rate when performing supervised tuning.	Remote models over Google models in Vertex AI
ACCELERATOR_TYPE	The type of accelerator to use when performing supervised tuning.	Remote models over Google models in Vertex AI
EVALUATION_TASK	When performing supervised tuning, the type of task that you want to tune the model to perform.	Remote models over Google models in Vertex AI
DOCUMENT_PROCESSOR	Identifies the document processor to use when the REMOTE_SERVICE_TYPE option value is CLOUD_AI_DOCUMENT_V1.	Remote models over Cloud AI services
SPEECH_RECOGNIZER	Identifies the speech recognizer to use when the REMOTE_SERVICE_TYPE option value is CLOUD_AI_SPEECH_TO_TEXT_V2	Remote models over Cloud AI services
KMS_KEY_NAME	Specifies the Cloud Key Management Service customer-managed encryption key (CMEK) to use to encrypt the model.	Linear & logistic regression, Boosted trees, Random forest, DNN, Wide & Deep, AutoML, K-means, PCA, Autoencoder, Matrix factorization, ARIMA_PLUS, ARIMA_PLUS_XREG , ONNX, TensorFlow, TensorFlow Lite, XGBoost
CONTRIBUTION_METRIC	The expression to use when performing contribution analysis.	Contribution analysis
DIMENSION_ID_COLS	The names of the columns to use as dimensions when summarizing the contribution analysis metric.	Contribution analysis
IS_TEST_COL	The name of the column to use to determine whether a given row is test data or control data.	Contribution analysis
MIN_APRIORI_SUPPORT	The minimum apriori support threshold for including segments in the model output.	Contribution analysis

AS

All model types support the following AS clause syntax for specifying the training data:

AS query_statement

For time series forecasting models that have a DATA_FREQUENCY value of either DAILY or AUTO_FREQUENCY, you can optionally use the following AS clause syntax to perform custom holiday modeling in addition to specifying the training data:

AS (
  training_data AS (query_statement),
  custom_holiday AS (holiday_statement)
)

query_statement

The query_statement argument specifies the query that is used to generate the training data. For information about the supported SQL syntax of the query_statement clause, see GoogleSQL query syntax.

holiday_statement

The holiday_statement argument specifies the query that provides custom holiday modeling information for time series forecast models. This query must return 50,000 rows or less and must contain the following columns:

• region: Required. A STRING value that identifies the region to target for holiday modeling. Use one of the following options:

  • An upper-case holiday region code. Use this option to overwrite or supplement the holidays for the specified region. You can see the holidays for a region by running SELECT * FROM bigquery-public-data.ml_datasets.holidays_and_events_for_forecasting WHERE region = region.
  • An arbitrary string. Use this option to specify a custom region that you want to model holidays for. For example, you could specify London if you are only modeling holidays for that city.

  Be sure not to use an existing holiday region code when you are trying to model for a custom region. For example, if you want to model a holiday in California, and specify CA as the region value, the service recognizes that as the holiday region code for Canada and targets that region. Because the argument is case-sensitive, you could specify ca, California, or some other value that isn't a holiday region code.

• holiday_name: Required. A STRING value that identifies the holiday to target for holiday modeling. Use one of the following options:

  • The holiday name as it is represented in the bigquery-public-data.ml_datasets.holidays_and_events_for_forecasting public table, including case. Use this option to overwrite or supplement the specified holiday.
  • A string that represents a custom holiday. The string must be a valid column name so that it can be used in ML.EXPLAIN_FORECAST output. For example, it cannot contain space. For more information on column naming, see Column names.

• primary_date: Required. A DATE value that specifies the date the holiday falls on.

• preholiday_days: Optional. An INT64 value that specifies the start of the holiday window around the holiday that is taken into account when modeling. Must be greater than or equal to 1. Defaults to 1.

• postholiday_days: Optional. An INT64 value that specifies the end of the holiday window around the holiday that is taken into account when modeling. Must be greater than or equal to 1. Defaults to 1.

The preholiday_days and postholiday_days arguments together describe the holiday window around the holiday that is taken into account when modeling. The holiday window is defined as [primary_date - preholiday_days, primary_date + postholiday_days] and is inclusive of the pre- and post-holiday days. The value for each holiday window must be less than or equal to 30 and must be the same across the given holiday. For example, if you are modeling Arbor Day for several different years, you must specify the same holiday window for all of those years.

To achieve the best holiday modeling result, provide as much historical and forecast information about the occurrences of each included holiday as possible. For example, if you have time series data from 2018 to 2022 and would like to forecast for 2023, you get the best result by providing the custom holiday information for all of those years, similar to the following:

CREATE OR REPLACE MODEL `mydataset.arima_model`
  OPTIONS (
    model_type = 'ARIMA_PLUS',
    holiday_region = 'US',...) AS (
        training_data AS (SELECT * FROM `mydataset.timeseries_data`),
        custom_holiday AS (
            SELECT
              'US' AS region,
              'Halloween' AS holiday_name,
              primary_date,
              5 AS preholiday_days,
              1 AS postholiday_days
            FROM
              UNNEST(
                [
                  DATE('2018-10-31'),
                  DATE('2019-10-31'),
                  DATE('2020-10-31'),
                  DATE('2021-10-31'),
                  DATE('2022-10-31'),
                  DATE('2023-10-31')])
                AS primary_date
          )
      )

Supported inputs

The CREATE MODEL statement supports the following data types for input label, data split columns and input feature columns.

Supported input feature types

See Supported input feature types for BigQuery ML supported input feature types.

Supported data types for input label columns

BigQuery ML supports different GoogleSQL data types depending on the model type. Supported data types for input_label_cols include:

Model type	Supported label types
regression models	INT64 NUMERIC BIGNUMERIC FLOAT64
classification models	Any groupable data type

Supported data types for data split columns

BigQuery ML supports different GoogleSQL data types depending on the data split method. Supported data types for data_split_col include:

Data split method	Supported column types
CUSTOM	BOOL
SEQ	INT64 NUMERIC BIGNUMERIC FLOAT64 TIMESTAMP

Limitations

CREATE MODEL statements must comply with the following rules:

• Only one CREATE statement is allowed.
• When you use a CREATE MODEL statement, the size of the model must be 90 MB or less or the query fails. Generally, if all categorical variables are short strings, a total feature cardinality (model dimension) of 5-10 million is supported. The dimensionality is dependent on the cardinality and length of the string variables.
• The label column cannot contain NULL values. If the label column contains NULL values, then the query fails.
• The CREATE MODEL IF NOT EXISTS clause always updates the last modified timestamp of a model.
• Query statements used in the CREATE MODEL statement cannot contain EXTERNAL_QUERY. If you want to use EXTERNAL_QUERY, then materialize the query result and then use the CREATE MODEL statement with the newly created table.