The CREATE MODEL statement for generalized linear models

This document describes the CREATE MODEL statement for creating linear regression or logistic regression models in BigQuery.

You can use linear regression models with the ML.PREDICT function to perform regression, and you can use logistic regression models with the ML.PREDICT function to perform classification. You can use both linear and logistic regression 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` syntax

{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 = { 'LINEAR_REG' | 'LOGISTIC_REG' }
    [, OPTIMIZE_STRATEGY = { 'AUTO_STRATEGY' | 'BATCH_GRADIENT_DESCENT' | 'NORMAL_EQUATION' } ]
    [, LEARN_RATE_STRATEGY = { 'LINE_SEARCH' | 'CONSTANT' } ]
    [, LEARN_RATE = float64_value ]
    [, LS_INIT_LEARN_RATE = float64_value ]
    [, CALCULATE_P_VALUES = { TRUE | FALSE } ]
    [, FIT_INTERCEPT = { TRUE | FALSE } ]
    [, CATEGORY_ENCODING_METHOD = { 'ONE_HOT_ENCODING` | 'DUMMY_ENCODING' } ]
    [, AUTO_CLASS_WEIGHTS = { TRUE | FALSE } ]
    [, CLASS_WEIGHTS = struct_array ]
    [, ENABLE_GLOBAL_EXPLAIN = { TRUE | FALSE } ]
    [, INPUT_LABEL_COLS = string_array ]
    [, L1_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, L2_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, MAX_ITERATIONS = int64_value ]
    [, WARM_START = { TRUE | FALSE } ]
    [, 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 ]
    [, 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' | ... } ]
    [, MODEL_REGISTRY = { 'VERTEX_AI' } ]
    [, VERTEX_AI_MODEL_ID = string_value ]
    [, VERTEX_AI_MODEL_VERSION_ALIASES = string_array ]
    [, KMS_KEY_NAME = string_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 doesn't 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`

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`.

`MODEL_TYPE`

Syntax

MODEL_TYPE = { 'LINEAR_REG' | 'LOGISTIC_REG'}

Description

Specify the model type. This option is required.

Arguments

This option accepts the following values:

LINEAR_REG: The model performs linear regression for forecasting; for example, the sales of an item on a given day. Labels are real-valued. They can't be +/- infinity or NaN.
LOGISTIC_REG: The model performs logistic regression for classification; for example, determining whether a customer will make a purchase.

Logistic models can be one of two types:
- Binary logistic regression for classification; for example, determining whether a customer will make a purchase. Labels must only have two possible values, one for the positive class and another for the negative class. BigQuery ML treats the higher label value as the positive class, and lower label value as the negative class. This holds for both numeric and string label values.
- Multiclass logistic regression for classification; for example, predicting multiple possible values such as whether an input is low-value, medium-value, or high-value. Labels can have up to 50 unique values. In BigQuery ML, multiclass logistic regression training uses a multinomial classifier with a cross entropy loss function.

`OPTIMIZE_STRATEGY`

Syntax

OPTIMIZE_STRATEGY = { 'AUTO_STRATEGY' | 'BATCH_GRADIENT_DESCENT' | 'NORMAL_EQUATION' }

Description

The strategy to train linear regression models.

Arguments

This option accepts the following values:

AUTO_STRATEGY: This is the default. Determines the training strategy as follows:
- If you specified a value for L1_REG or set WARM_START to TRUE, the BATCH_GRADIENT_DESCENT strategy is used.
- If the total cardinality of training features is more than 10,000, the BATCH_GRADIENT_DESCENT strategy is used.
- If there is an over-fitting issue, where the number of training examples is less than 10x and x is the total cardinality, the BATCH_GRADIENT_DESCENT strategy is used.
- The NORMAL_EQUATION strategy is used for all other cases.
BATCH_GRADIENT_DESCENT: Train the model using the batch gradient descent method, which optimizes the loss function using the gradient function.
NORMAL_EQUATION: Directly compute the least square solution of the linear regression problem with the analytical formula. You can't use NORMAL_EQUATION in the following cases:
- You specified a value for L1_REG.
- You set WARM_START to TRUE.
- The total cardinality of training features is greater than 10,000.

`LEARN_RATE_STRATEGY`

Syntax

LEARN_RATE_STRATEGY = { 'LINE_SEARCH' | 'CONSTANT' }

Description

The strategy for specifying the learning rate during training.

Arguments

This option accepts the following values:

LINE_SEARCH: This is the default. Use the line search method to calculate the learning rate. You specify the line search initial learn rate in LS_INIT_LEARN_RATE.

Line search slows down training and increases the number of bytes processed, but it generally converges even with a larger initial specified learning rate.
CONSTANT: Set the learning rate to the value you specify in LEARN_RATE.

`LEARN_RATE`

Syntax

LEARN_RATE = float64_value

Description

The learn rate for gradient descent when LEARN_RATE_STRATEGY is set to CONSTANT. If LEARN_RATE_STRATEGY is set to LINE_SEARCH, an error is returned.

Arguments

A FLOAT64 value. The default value is 0.1.

`LS_INIT_LEARN_RATE`

Syntax

LS_INIT_LEARN_RATE = float64_value

Description

Sets the initial learning rate when you specify LINE_SEARCH for LEARN_RATE_STRATEGY.

If the model learning rate appears to be doubling every iteration as indicated by the ML.TRAINING_INFO function, then try setting LS_INIT_LEARN_RATE to the last doubled learning rate. The optimal initial learning rate is different for every model. A good initial learning rate for one model might not be a good initial learning rate for another.

Arguments

A FLOAT64 value. The default value is 0.1.

`CALCULATE_P_VALUES`

Syntax

CALCULATE_P_VALUES = { TRUE | FALSE }

Description

Determines whether to compute p-values and standard errors during training.

P-values and standard errors are computed when you create the model. This option must be TRUE if you want to use the ML.ADVANCED_WEIGHTS function to retrieve the p-values and standard errors after the model finishes training. For more information on the usage requirements for ML.ADVANCED_WEIGHTS, see Usage requirements.

Arguments

A BOOL value. The default value is FALSE.

`FIT_INTERCEPT`

Syntax

FIT_INTERCEPT = { TRUE | FALSE }

Description

Determines whether to fit an intercept to the model during training.

Arguments

A BOOL value. The default value is TRUE.

`CATEGORY_ENCODING_METHOD`

Syntax

CATEGORY_ENCODING_METHOD = { 'ONE_HOT_ENCODING' | 'DUMMY_ENCODING' }

Description

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

Arguments

This option accepts the following values:

ONE_HOT_ENCODING. This is the default.
DUMMY_ENCODING

`AUTO_CLASS_WEIGHTS`

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 logistic regression 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.

`CLASS_WEIGHTS`

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)]

`ENABLE_GLOBAL_EXPLAIN`

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.

`INPUT_LABEL_COLS`

Syntax

INPUT_LABEL_COLS = string_array

Description

The name of the label column in the training data.

Arguments

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

`L1_REG`

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.

`L2_REG`

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 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.

`MAX_ITERATIONS`

Syntax

MAX_ITERATIONS = int64_value

Description

The maximum number of training iterations, where one iteration represents a single pass of the entire training data.

Arguments

An INT64 value. The default value is 20.

`WARM_START`

Syntax

WARM_START = { TRUE | FALSE }

Description

Determines whether to train a model with new training data, new model options, or both. Unless you explicitly override them, the initial options used to train the model are used for the warm start run.

In a warm start run, the iteration numbers are reset to start from zero. Use the training run or iteration information returned by the ML.TRAINING_INFO function to distinguish the warm start run from the original run.

The values of the MODEL_TYPE and LABELS options and the training data schema must remain constant in a warm start.

Arguments

A BOOL value. The default value is FALSE.

`EARLY_STOP`

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.

`MIN_REL_PROGRESS`

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.

`DATA_SPLIT_METHOD`

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:

* AUTO_SPLIT: 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.

`DATA_SPLIT_EVAL_FRACTION`

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.

`DATA_SPLIT_TEST_FRACTION`

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.

`DATA_SPLIT_COL`

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.

`NUM_TRIALS`

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.

`MAX_PARALLEL_TRIALS`

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.

`HPARAM_TUNING_ALGORITHM`

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.

`HPARAM_TUNING_OBJECTIVES`

Syntax

For LINEAR_REG models:

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

For LOGISTIC_REG models:

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

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, then the default objective is used. For LINEAR_REG models, the default is R2_SCORE. For LOGISTIC_REG models, the default is ROC_AUC.

`MODEL_REGISTRY`

The MODEL_REGISTRY option specifies the model registry destination. VERTEX_AI is the only supported model registry destination. To learn more, see Register a BigQuery ML model.

`VERTEX_AI_MODEL_ID`

The VERTEX_AI_MODEL_ID option specifies 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.

`VERTEX_AI_MODEL_VERSION_ALIASES`

The VERTEX_AI_MODEL_VERSION_ALIASES option specifies 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 ID.

`KMS_KEY_NAME`

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'

`query_statement`

The AS query_statement clause specifies the GoogleSQL query used to generate the training data. See the GoogleSQL query syntax page for the supported SQL syntax of the query_statement clause.

All columns referenced by the query_statement are used as inputs to the model except for the columns included in INPUT_LABEL_COLS and DATA_SPLIT_COL.

Hyperparameter tuning

Linear and logistic 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 linear regression models, see LINEAR_REG. For more information about the training objectives and hyperparameters supported for logistic regression models, see LOGISTIC_REG. To try a tutorial that walks you through hyperparameter tuning, see Improve model performance with hyperparameter tuning.

Limitations

CREATE MODEL statements must comply with the following rules:

For linear regression models, the label column must be real-valued (the column values cannot be +/- infinity or NaN).
For logistic regression models, the label column can contain up to 50 unique values; that is, the number of classes is less than or equal to 50. If you need to classify into more than 50 labels, contact bqml-feedback@google.com.

Examples

The following examples create models named mymodel in mydataset in your default project.

Train a linear regression model

The following example creates and trains a linear regression model. The learn rate is set to 0.15, the L1 regularization is set to 1, and the maximum number of training iterations is set to 5.

CREATE MODEL
  `mydataset.mymodel`
OPTIONS
  ( MODEL_TYPE='LINEAR_REG',
    LS_INIT_LEARN_RATE=0.15,
    L1_REG=1,
    MAX_ITERATIONS=5 ) AS
SELECT
  column1,
  column2,
  column3,
  label
FROM
  `mydataset.mytable`
WHERE
  column4 < 10

Train a linear regression model with a sequential data split

The following example creates a linear regression model with a sequential data split. The split fraction is 0.3 and the split uses the timestamp column as the basis for the split.

CREATE MODEL
  `mydataset.mymodel`
OPTIONS
  ( MODEL_TYPE='LINEAR_REG',
    LS_INIT_LEARN_RATE=0.15,
    L1_REG=1,
    MAX_ITERATIONS=5,
    DATA_SPLIT_METHOD='SEQ',
    DATA_SPLIT_EVAL_FRACTION=0.3,
    DATA_SPLIT_COL='timestamp' ) AS
SELECT
  column1,
  column2,
  column3,
  timestamp,
  label
FROM
  `mydataset.mytable`
WHERE
  column4 < 10

Train a linear regression model with a custom data split

The following example creates a linear regression model using a custom data split method and trains the model by joining the data from the evaluation and training tables. All the columns in the training table and in the evaluation table are either features or the label. The query uses SELECT * and UNION ALL to append all of the data in the split_col column to the existing data.

CREATE MODEL
  `mydataset.mymodel`
OPTIONS
  ( MODEL_TYPE='LINEAR_REG',
    DATA_SPLIT_METHOD='CUSTOM',
    DATA_SPLIT_COL='SPLIT_COL' ) AS
SELECT
  *,
  false AS split_col
FROM
  `mydataset.training_table`
UNION ALL
SELECT
  *,
  true AS split_col
FROM
  `mydataset.evaluation_table`

Train a multiclass logistic regression model with automatically calculated weights

The following example creates a multiclass logistic regression model using the auto_class_weights option.

CREATE MODEL
  `mydataset.mymodel`
OPTIONS
  ( MODEL_TYPE='LOGISTIC_REG',
    AUTO_CLASS_WEIGHTS=TRUE ) AS
SELECT
  *
FROM
  `mydataset.mytable`

Train a multiclass logistic regression model with specified weights

The following example creates a multiclass logistic regression model using the class_weights option. The label columns are label1, label2, and label3.

CREATE MODEL
  `mydataset.mymodel`
OPTIONS
  ( MODEL_TYPE='LOGISTIC_REG',
    CLASS_WEIGHTS=[('label1', 0.5), ('label2', 0.3), ('label3', 0.2)]) AS
SELECT
  *
FROM
  `mydataset.mytable`

Train a logistic regression model with specified weights

The following example creates a logistic regression model using the class_weights option.

CREATE MODEL
  `mydataset.mymodel`
OPTIONS
  ( MODEL_TYPE='LOGISTIC_REG',
    CLASS_WEIGHTS=[('0', 0.9), ('1', 0.1)]) AS
SELECT
  *
FROM
  `mydataset.mytable`

Model creation with `TRANSFORM`, while excluding original columns

The following example trains a model after adding the columns f1 and f2 from the SELECT statement to form a new column c; the columns f1 and f2 are omitted from the training data. Model training uses columns f3 and label_col as they appear in the data source t.

CREATE MODEL `mydataset.mymodel`
  TRANSFORM(f1 + f2 as c, * EXCEPT(f1, f2))
  OPTIONS(model_type='linear_reg', input_label_cols=['label_col'])
AS SELECT f1, f2, f3, label_col FROM t;

The CREATE MODEL statement for generalized linear models

CREATE MODEL syntax

CREATE MODEL

CREATE MODEL IF NOT EXISTS

CREATE OR REPLACE MODEL

model_name

MODEL_TYPE

OPTIMIZE_STRATEGY

LEARN_RATE_STRATEGY

LEARN_RATE

LS_INIT_LEARN_RATE

CALCULATE_P_VALUES

FIT_INTERCEPT

CATEGORY_ENCODING_METHOD

AUTO_CLASS_WEIGHTS

CLASS_WEIGHTS

ENABLE_GLOBAL_EXPLAIN

INPUT_LABEL_COLS

L1_REG

L2_REG

MAX_ITERATIONS

WARM_START

EARLY_STOP

MIN_REL_PROGRESS

DATA_SPLIT_METHOD

DATA_SPLIT_EVAL_FRACTION

DATA_SPLIT_TEST_FRACTION

DATA_SPLIT_COL

NUM_TRIALS

MAX_PARALLEL_TRIALS

HPARAM_TUNING_ALGORITHM

HPARAM_TUNING_OBJECTIVES

MODEL_REGISTRY

VERTEX_AI_MODEL_ID

VERTEX_AI_MODEL_VERSION_ALIASES

KMS_KEY_NAME

query_statement

Hyperparameter tuning

Limitations

Examples

Train a linear regression model

Train a linear regression model with a sequential data split

Train a linear regression model with a custom data split

Train a multiclass logistic regression model with automatically calculated weights

Train a multiclass logistic regression model with specified weights

Train a logistic regression model with specified weights

Model creation with TRANSFORM, while excluding original columns

What's next

`CREATE MODEL` syntax

`CREATE MODEL`

`CREATE MODEL IF NOT EXISTS`

`CREATE OR REPLACE MODEL`

`model_name`

`MODEL_TYPE`

`OPTIMIZE_STRATEGY`

`LEARN_RATE_STRATEGY`

`LEARN_RATE`

`LS_INIT_LEARN_RATE`

`CALCULATE_P_VALUES`

`FIT_INTERCEPT`

`CATEGORY_ENCODING_METHOD`

`AUTO_CLASS_WEIGHTS`

`CLASS_WEIGHTS`

`ENABLE_GLOBAL_EXPLAIN`

`INPUT_LABEL_COLS`

`L1_REG`

`L2_REG`

`MAX_ITERATIONS`

`WARM_START`

`EARLY_STOP`

`MIN_REL_PROGRESS`

`DATA_SPLIT_METHOD`

`DATA_SPLIT_EVAL_FRACTION`

`DATA_SPLIT_TEST_FRACTION`

`DATA_SPLIT_COL`

`NUM_TRIALS`

`MAX_PARALLEL_TRIALS`

`HPARAM_TUNING_ALGORITHM`

`HPARAM_TUNING_OBJECTIVES`

`MODEL_REGISTRY`

`VERTEX_AI_MODEL_ID`

`VERTEX_AI_MODEL_VERSION_ALIASES`

`KMS_KEY_NAME`

`query_statement`

Model creation with `TRANSFORM`, while excluding original columns