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. Linear and logistic regression models models
support hyperparameter tuning.
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.DETECT_ANOMALIES
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 orNaN
.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
, orhigh-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 setWARM_START
toTRUE
, theBATCH_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.
- If you specified a value for
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 useNORMAL_EQUATION
in the following cases:- You specified a value for
L1_REG
. - You set
WARM_START
toTRUE
. - The total cardinality of training features is greater than 10,000.
- You specified a value for
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 inLS_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 inLEARN_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 twoFLOAT64
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 ofFLOAT64
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 twoFLOAT64
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 ofFLOAT64
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 theDATA_SPLIT_EVAL_FRACTION
andDATA_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 theAUTO_SPLIT
option.A random split is deterministic: different training runs produce the same split results if the same underlying training data is used.
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 ofTRUE
orNULL
are used as evaluation data, rows with a value ofFALSE
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 ofTRAIN
are used as training data, rows with a value ofEVAL
are used as evaluation data, and rows with a value ofTEST
are used as test data.
Use the
DATA_SPLIT_COL
option to identify the column that contains the data split information.- If you aren't running hyperparameter tuning, then you must provide the
name of a column of type
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 forDATA_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 forDATA_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 forDATA_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 forDATA_SPLIT_METHOD
, then you must provide the name of a column of typeBOOL
. Rows with a value ofTRUE
orNULL
are used as evaluation data, rows with a value ofFALSE
are used as training data. - If you are running hyperparameter tuning and you are specifying
SEQ
as the value forDATA_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 forDATA_SPLIT_EVAL_FRACTION
. The next m rows are used as test data, where m is the value specified forDATA_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 forDATA_SPLIT_METHOD
, then you must provide the name of a column of typeSTRING
. Rows with a value ofTRAIN
are used as training data, rows with a value ofEVAL
are used as evaluation data, and rows with a value ofTEST
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
.
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 orNaN
). - For logistic regression models, the label columns can contain up to 50 unique values; that is, the number of classes is less than or equal to 50.
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;
What's next
- Create a regression model.
- Create a classification model.
- Learn more about hyperparameter tuning.
- Use hyperparameter tuning to improve model performance.