# The CREATE MODEL statement for Wide-and-Deep models

This document describes the `CREATE MODEL` statement for creating
[wide-and-deep](https://dl.acm.org/doi/10.1145/2988450.2988454)
models in BigQuery by using SQL. Alternatively, you can use the
Google Cloud console user interface to
[create a model by using a UI](https://docs.cloud.google.com/bigquery/docs/create-machine-learning-model-console)
([Preview](https://cloud.google.com/products#product-launch-stages)) instead of constructing the SQL
statement yourself.

You can use wide-and-deep regressor models with the
[`ML.PREDICT` function](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict)
to perform [regression](https://docs.cloud.google.com/bigquery/docs/regression-overview), and you can use
wide-and-deep classifier models with the `ML.PREDICT` function to
perform [classification](https://docs.cloud.google.com/bigquery/docs/classification-overview). You can use
both types of wide-and-deep models with the
`ML.PREDICT` function
to perform [anomaly detection](https://docs.cloud.google.com/bigquery/docs/anomaly-detection-overview).

For more information about supported SQL statements and functions for this
model, see
[End-to-end user journeys for ML models](https://docs.cloud.google.com/bigquery/docs/e2e-journey).

## `CREATE MODEL` syntax

```sql
{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 = { 'DNN_LINEAR_COMBINED_CLASSIFIER' | 'DNN_LINEAR_COMBINED_REGRESSOR' }
    [, LEARN_RATE = float64_value | struct_array ]
    [, OPTIMIZER = string_value | struct_array ]
    [, L1_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, L2_REG = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, ACTIVATION_FN = { { 'RELU' | 'RELU6' | 'CRELU' | 'ELU' | 'SELU' | 'SIGMOID' | 'TANH' } | HPARAM_CANDIDATES([candidates]) } ]
    [, BATCH_SIZE = { int64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, DROPOUT = { float64_value | HPARAM_RANGE(range) | HPARAM_CANDIDATES([candidates]) } ]
    [, HIDDEN_UNITS = { int_array | HPARAM_CANDIDATES([candidates]) } ]
    [, AUTO_CLASS_WEIGHTS = { TRUE | FALSE } ]
    [, CLASS_WEIGHTS = struct_array ]
    [, EARLY_STOP = { TRUE | FALSE } ]
    [, MIN_REL_PROGRESS = float64_value ]
    [, INPUT_LABEL_COLS = string_array ]
    [, MAX_ITERATIONS = int64_value ]
    [, WARM_START = { TRUE | FALSE } ]
    [, 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 ]
```

### `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 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`

    MODEL_TYPE = { 'DNN_LINEAR_COMBINED_CLASSIFIER' | 'DNN_LINEAR_COMBINED_REGRESSOR' }

**Description**

Specifies the model type. This option is required.

### `LEARN_RATE`

**Syntax**

`
LEARN_RATE = float64_value | struct_array
`

**Description**

The initial learn rate for training.

**Arguments**

To specify an initial learn rate for both the linear and DNN parts of the model,
provide a `FLOAT64` value. For example:

    LEARN_RATE = 0.01

To specify different initial learn rates for the linear and DNN parts of the
model, provide an array of `STRUCT` values that each contain one `STRING` and
one `FLOAT64` value. The string identifies the part of the model, and the float
specifies the initial learn rate to use for that part of the model.
For example:

    LEARN_RATE = [STRUCT('dnn', 0.001), STRUCT('linear', 0.01)]

The default value is `0.001` for both the linear and DNN parts of the model.

### `OPTIMIZER`

**Syntax**

`
OPTIMIZER = string_value | struct_array
`

**Description**

Specifies the optimizer for training the model.

**Arguments**

To specify an optimizer for both the linear and DNN parts of the model,
provide a `STRING` value. For example:

    OPTIMIZER = 'ADAGRAD'

To specify different optimizers for the linear and DNN parts of the
model, provide an array of `STRUCT` values that each contain two `STRING`
values. The first string identifies the part of the model, and the second string
specifies the optimizer to use for that part of the model. For example:

    OPTIMIZER = [STRUCT('dnn', 'ADAGRAD'), STRUCT('linear', 'SGD')]

Use one of the following values for the optimizer string:

- `ADAGRAD` --- [Implements the Adagrad algorithm](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/AdagradOptimizer)
- `ADAM` --- [Implements the Adam algorithm](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/AdamOptimizer)
- `FTRL` --- [Implements the FTRL algorithm](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/FtrlOptimizer)
- `RMSPROP` --- [Implements the RMSProp algorithm](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/RMSPropOptimizer)
- `SGD` --- [Implements the gradient descent algorithm](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/GradientDescentOptimizer)

The default value of `OPTIMIZER` is
`[STRUCT('dnn', 'ADAM'), STRUCT('linear', 'FTRL')]`.

### `L1_REG`

**Syntax**

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

**Description**

The L1 regularization strength of the `OPTIMIZER`. You can only use
this option when `OPTIMIZER` is set to one of the following values:

- `ADAGRAD` --- [Implements the ProximalAdagradOptimizer algorithm](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/ProximalAdagradOptimizer)
- `FTRL` --- [Implements the FtrlOptimizer algorithm](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/FtrlOptimizer)
- `SGD` --- [Implements the ProximalGradientDescentOptimizer algorithm](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/ProximalGradientDescentOptimizer)

**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 L2 regularization strength of the `OPTIMIZER`. You can only
use this option when `OPTIMIZER` is set to one of the following values:

- `ADAGRAD` --- [Implements the ProximalAdagradOptimizer algorithm](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/ProximalAdagradOptimizer)
- `FTRL` --- [Implements the FtrlOptimizer algorithm](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/FtrlOptimizer)
- `SGD` --- [Implements the ProximalGradientDescentOptimizer algorithm](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/ProximalGradientDescentOptimizer)

**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`.

### `ACTIVATION_FN`

**Syntax**
`
ACTIVATION_FN = { { 'RELU' | 'RELU6' | 'CRELU' | 'ELU' | 'SELU' | 'SIGMOID' | 'TANH' } | HPARAM_CANDIDATES([candidates]) }
`

<br />

**Description**

The activation function of the neural network.

**Arguments**

This option accepts the following values:

- `RELU` --- [Rectified linear](https://www.tensorflow.org/api_docs/python/tf/nn/relu). This is the default.
- `RELU6` --- [Rectified linear 6](https://www.tensorflow.org/api_docs/python/tf/nn/relu6)
- `CRELU` --- [Concatenated ReLU](https://www.tensorflow.org/api_docs/python/tf/nn/crelu)
- `ELU` --- [Exponential linear](https://www.tensorflow.org/api_docs/python/tf/nn/elu)
- `SELU` --- [Scaled exponential linear](https://www.tensorflow.org/api_docs/python/tf/nn/selu)
- `SIGMOID` --- [Sigmoid activation](https://www.tensorflow.org/api_docs/python/tf/math/sigmoid)
- `TANH` --- [Tanh activation](https://www.tensorflow.org/api_docs/python/tf/math/tanh)

If you are running hyperparameter training, then you can provide more than one
value for this option by using `HPARAM_CANDIDATES` and specifying an array.
For example, `ACTIVATION_FN = HPARAM_CANDIDATES(['RELU', 'RELU6', 'TANH'])`.

### `BATCH_SIZE`

**Syntax**

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

**Description**

The mini batch size of samples that are fed to the neural network.

**Arguments**

If you aren't running hyperparameter tuning, specify an `INT64` value that
is positive and is less than or equal to `8192`. The default value is `32`
or the number of samples, whichever is smaller.

If you are running hyperparameter tuning, 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, `BATCH_SIZE = HPARAM_RANGE(16, 64)`.
- The `HPARAM_CANDIDATES` keyword and an array of `FLOAT64` values that provide discrete values to use for the hyperparameter. For example, `BATCH_SIZE = HPARAM_CANDIDATES([32, 64, 256, 1024])`.

When running hyperparameter tuning, the valid range is `(0, ∞)`,
the default range is `[16, 1024]`, and the scale type is `LOG`.

### `DROPOUT`

**Syntax**

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

**Description**

The dropout rate of units in the neural network.

**Arguments**

If you aren't running hyperparameter tuning, then you can specify a `FLOAT64`
value that is positive and is less than or equal to `1.0`. The default value is
`0`.

If you are running hyperparameter tuning, then you must 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, `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([0.1, 0.3, 0.6])`.

When running hyperparameter tuning, the valid range is `[0, 1.0)`, the
default range is `[0, 0.8]`, and the scale type is `LINEAR`.

### `HIDDEN_UNITS`

**Syntax**

`
HIDDEN_UNITS = { int_array | HPARAM_CANDIDATES([candidates]) }
`

**Description**

The hidden layers of the neural network.

**Arguments**

An array of integers that represents the architecture of the hidden layers. If
not specified, BigQuery ML applies a single hidden layer that
contains no more than 128 units. The number of units is calculated as `[min(128,
num_samples / (10 * (num_input_units + num_output_units)))]`. The upper bound of
the rule ensures that the model isn't overfitting.

The number in the middle of the array defines the shape of the latent space. For
example, `hidden_units=[128, 64, 4, 64, 128]` defines a four-dimensional latent
space.

The number of layers in `hidden_units` must be odd, and we recommend that the
sequence be symmetrical.

The following example defines a model architecture that uses three hidden layers
with 256, 128, and 64 nodes, respectively.

    HIDDEN_UNITS = [256, 128, 64]

If you are running hyperparameter tuning, then you must use the
`HPARAM_CANDIDATES` keyword and specify an array in the form
`ARRAY<STRUCT<ARRAY<INT64>>>` to provide discrete values to use for the
hyperparameter. Each struct value in the outer array represents a candidate
neural architecture. The array of `INT64` values in each struct represents a
hidden layer.

The following example represents a neural architecture search with three
candidates, which include a single layer of 8 neurons, two layers of neurons
with 8 and 16 in sequence, and three layers of neurons with 16, 32 and 64 in
sequence, respectively.

    hidden_units=hparam_candidates([struct([8]), struct([8, 16]), struct([16, 32, 64])])

The valid range for the `INT64` arrays is `[1, ∞)`.

### `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 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`.

### `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`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#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)]

### `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`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#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`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#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`.

### `INPUT_LABEL_COLS`

**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`.

### `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](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-train)
to distinguish the warm start run from the original run.

In a warm start, the values of the `MODEL_TYPE`, `LABELS`, and `HIDDEN_UNITS`
options, and the training data schema, must remain the same as they were in
previous training job.

**Arguments**

A `BOOL` value. The default value is `FALSE`.

### `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](https://developers.google.com/machine-learning/glossary/#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](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/models#datasplitresult) 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](https://docs.cloud.google.com/bigquery/docs/hp-tuning-overview#data_split).
- `RANDOM`: Data is randomized before being split into sets. You can use this
  option with the [`DATA_SPLIT_EVAL_FRACTION`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#data_split_eval_fraction) and
  [`DATA_SPLIT_TEST_FRACTION`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#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]
  > **Note:** A random split is based on the [FARM_FINGERPRINT](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/hash_functions#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](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#data_split_col) 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 last <var translate="no">n</var> rows
  are used as evaluation data, where <var translate="no">n</var> is the value specified for
  `DATA_SPLIT_EVAL_FRACTION`. The remaining rows are used as training data.

  When you are running hyperparameter tuning, the last <var translate="no">m</var> rows
  are used as test data, where <var translate="no">m</var> is the value specified for
  `DATA_SPLIT_TEST_FRACTION`. The second last <var translate="no">n</var> rows are used as
  evaluation data, where <var translate="no">n</var> is the value specified for
  `DATA_SPLIT_EVAL_FRACTION`. The remaining rows are used as training data.

  All rows with split values smaller than the threshold, including `NULL`s,
  are used as training data. The remaining rows 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](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#data_split_method).

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`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#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](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#data_split_method).

If you specify a value for this option, you must also specify a value for
[`DATA_SPLIT_EVAL_FRACTION`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#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](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#data_split_method):

- 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 <var translate="no">n</var> rows are used as evaluation data, where <var translate="no">n</var> is the value specified for [`DATA_SPLIT_EVAL_FRACTION`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#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 `NULL` are 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 <var translate="no">m</var> rows are used as test data, where <var translate="no">m</var> is the value specified for [`DATA_SPLIT_TEST_FRACTION`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#data_split_test_fraction). The second last <var translate="no">n</var> rows are used as evaluation data, where <var translate="no">n</var> is the value specified for [`DATA_SPLIT_EVAL_FRACTION`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#data_split_eval_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.

> [!NOTE]
> **Note:** We recommend using at least `(number_of_hyperparameters * 10)` trials to tune a model.

### `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`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#num_trials).

**Arguments**

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

> [!NOTE]
> **Note:** Although specifying larger `MAX_PARALLEL_TRIALS` values can accelerate the hyperparameter tuning process, acceleration can undermine the final model quality when you specify `VIZIER_DEFAULT` as the [`HPARAM_TUNING_ALGORITHM`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#hparam_tuning_algorithm) value. This is because the parallel trials can't benefit from concurrent training results.

### `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`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#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](https://en.wikipedia.org/wiki/Bayesian_optimization)
  with [Gaussian processes](https://en.wikipedia.org/wiki/Gaussian_process). It
  also uses
  [transfer learning](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-hyperparameter-tuning#transfer_learning)
  to take advantage of previously tuned models. This is the default, and also
  the recommended approach.

- `RANDOM_SEARCH`: Use
  [random search](https://en.wikipedia.org/wiki/Hyperparameter_optimization#Random_search)
  to explore the search space.

- `GRID_SEARCH`: Use
  [grid search](https://en.wikipedia.org/wiki/Hyperparameter_optimization#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 `DNN_LINEAR_COMBINED_CLASSIFIER` models:

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

For `DNN_LINEAR_COMBINED_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`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#num_trials).

**Arguments**

The possible objectives are a subset of the
[model evaluation metrics](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#output)
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 `DNN_LINEAR_COMBINED_CLASSIFIER` models, the default is `ROC_AUC`.
For `DNN_LINEAR_COMBINED_REGRESSOR` models, the default is `R2_SCORE`.

### `KMS_KEY_NAME`

**Syntax**

`
KMS_KEY_NAME = string_value
`

**Description**

The Cloud Key Management Service [customer-managed encryption key (CMEK)](https://docs.cloud.google.com/kms/docs/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'

### Internal parameter defaults

BigQuery ML uses the following default values when building
models:

    loss_reduction = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE

    batch_norm = False

### `query_statement`

The `AS query_statement` clause specifies the
GoogleSQL query used to generate the training data. See the
[GoogleSQL query syntax](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#sql_syntax)
page for the supported SQL syntax of the `query_statement` clause.

All columns referenced by the `query_statement` are used as input
[features](https://developers.google.com/machine-learning/glossary#feature)
to the model except for the columns included in
[`INPUT_LABEL_COLS`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#input_label_cols) and [`DATA_SPLIT_COL`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#data_split_col).

## Hyperparameter tuning

Wide-and-deep classification and regression models support
[hyperparameter tuning](https://docs.cloud.google.com/bigquery/docs/hp-tuning-overview), which you can use
to improve model performance for your data. To use
hyperparameter tuning, set the [`NUM_TRIALs` option](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#num_trials) 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](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models#hparam_tuning_objectives) to tune for
a different metric if you need to.

For more information about the training objectives and hyperparameters
supported for wide-and-deep classification models, see
[`DNN_LINEAR_COMBINED_CLASSIFIER`](https://docs.cloud.google.com/bigquery/docs/hp-tuning-overview#wnd_classifier).
For more information about the training objectives and hyperparameters
supported for wide-and-deep regression models, see
[`DNN_LINEAR_COMBINED_REGRESSOR`](https://docs.cloud.google.com/bigquery/docs/hp-tuning-overview#wnd_regressor).
To try a tutorial that walks you through hyperparameter tuning, see
[Improve model performance with hyperparameter tuning](https://docs.cloud.google.com/bigquery/docs/hyperparameter-tuning-tutorial).

## Locations

For information about supported locations, see
[Locations for non-remote models](https://docs.cloud.google.com/bigquery/docs/locations#locations-for-non-remote-models).

## Limitations

`CREATE MODEL` statements must comply with the following rules:

- For `DNN_LINEAR_COMBINED_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](mailto:bqml-feedback@google.com).

## Example

The following example trains a Wide-and-Deep classifier model against `'mytable'` with `'mylabel'` as the label column.

```sql
CREATE MODEL `project_id.mydataset.mymodel`
OPTIONS(MODEL_TYPE='DNN_LINEAR_COMBINED_CLASSIFIER',
        ACTIVATION_FN = 'RELU',
        BATCH_SIZE = 16,
        DROPOUT = 0.1,
        EARLY_STOP = FALSE,
        HIDDEN_UNITS = [128, 128, 128],
        INPUT_LABEL_COLS = ['mylabel'],
        LEARN_RATE=0.001,
        MAX_ITERATIONS = 50,
        OPTIMIZER = 'ADAGRAD')
AS SELECT * FROM `project_id.mydataset.mytable`;
```

## Supported regions

Training Wide-and-Deep models is not supported in all BigQuery ML
regions. For a complete list of supported regions and multi-regions, see
[BigQuery ML locations](https://docs.cloud.google.com/bigquery/docs/locations#bqml-loc) page.