Improve model performance with hyperparameter tuning

This tutorial shows how to use hyperparameter tuning in BigQuery ML by specifying the NUM_TRIALS training option to enable a set of model training trials.

In this tutorial, you use the tlc_yellow_trips_2018 sample table to create a model that predicts the tip of a taxi trip. With hyperparameter tuning, the model shows a ~40% performance improvement in the R2_SCORE hyperparameter tuning objective.


In this tutorial, you use:

  • BigQuery ML to create a linear regression model using the CREATE MODEL statement with the NUM_TRIALS set to 20.
  • The ML.TRIAL_INFO function to check the overview of all 20 trials
  • The ML.EVALUATE function to evaluate the ML model
  • The ML.PREDICT function to make predictions using the ML model


This tutorial uses billable components of Google Cloud, including:

  • BigQuery
  • BigQuery ML

For more information about BigQuery costs, see the BigQuery pricing page.

Before you begin

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  3. Make sure that billing is enabled for your Google Cloud project.

  4. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  5. Make sure that billing is enabled for your Google Cloud project.

  6. BigQuery is automatically enabled in new projects. To activate BigQuery in a pre-existing project, go to

    Enable the BigQuery API.

    Enable the API

Step one: Create your training dataset

Create a BigQuery dataset to store your ML model:

  1. In the Google Cloud console, go to the BigQuery page.

    Go to the BigQuery page

  2. In the Explorer pane, click your project name.

  3. Click View actions > Create dataset.

    Create dataset.

  4. On the Create dataset page, do the following:

    • For Dataset ID, enter bqml_tutorial.

    • For Location type, select Multi-region, and then select US (multiple regions in United States).

      The public datasets are stored in the US multi-region. For simplicity, store your dataset in the same location.

    • Leave the remaining default settings as they are, and click Create dataset.

      Create dataset page.

Step two: Create your training input table

In this step, you materialize the training input table with 100k rows.

  1. View the schema of the source table tlc_yellow_trips_2018.

    Table schema.

  2. Create the training input data table.

CREATE TABLE `bqml_tutorial.taxi_tip_input` AS
  * EXCEPT(tip_amount), tip_amount AS label
  tip_amount IS NOT NULL
LIMIT 100000

Step three: Create your model

Next, create a linear regression model with hyperparameter tuning using the tlc_yellow_trips_2018 sample table in BigQuery. The following GoogleSQL query is used to create the model with hyperparameter tuning.

CREATE MODEL `bqml_tutorial.hp_taxi_tip_model`

Query details

The LINEAR_REG model has two tunable hyperparameters: l1_reg and l2_reg. The above query uses the default search space. You can also specify the search space explicitly:

    L1_REG=HPARAM_RANGE(0, 20),
    L2_REG=HPARAM_CANDIDATES([0, 0.1, 1, 10]))

In addition, these other hyperparameter tuning training options also use their default values:


MAX_PARALLEL_TRIALS is set to 2 to accelerate the tuning process. With 2 trials running at any time, the whole tuning should take roughly as long as 10 serial training jobs instead of 20. Note, however, that the two concurrent trials cannot benefit from each other's training results.

Run the CREATE MODEL query

To run the CREATE MODEL query to create and train your model:

  1. In the Google Cloud console, click the Compose new query button.

  2. Enter the above GoogleSQL query in the Query editor text area.

  3. Click Run.

    The query takes about 17 minutes to complete. You can track the tuning progress in execution details under Stages:

    Tuning progress.

Step four: Get trials information

To see the overview of all trials including their hyperparameters, objectives, status, and the optimal trial, you can use the ML.TRIAL_INFO function, and you can view the result in the Google Cloud console after running the SQL.

  ML.TRIAL_INFO(MODEL `bqml_tutorial.hp_taxi_tip_model`)

You can run this SQL query as soon as one trial is done. If the tuning is stopped in the middle, all already-completed trials will remain available to use.

Step five: Evaluate your model

After creating your model, you can get the evaluation metrics of all trials by either using the ML.EVALUATE function or through Google Cloud console.


  ML.EVALUATE(MODEL `bqml_tutorial.hp_taxi_tip_model`)

This SQL fetches evaluation metrics for all trials calculated from the TEST data. For more information about the difference between ML.TRIAL_INFO objectives and ML.EVALUATE evaluation metrics, see Model serving functions.

You can also evaluate a specific trial by providing your own data. For more information, see Model serving functions.

Check evaluation metrics through Google Cloud console

You can also check evaluation metrics by selecting the EVALUATION tab.

Tuning evaluation.

Step six: Use your model to predict taxi tips

Now that you have evaluated your model, the next step is to use it to predict the taxi tip.

The query used to predict the tip is as follows:

  ML.PREDICT(MODEL `bqml_tutorial.hp_taxi_tip_model`,
    LIMIT 10))

Query details

The top-most SELECT statement retrieves all columns including the predicted_label column. This column is generated by the ML.PREDICT function. When you use the ML.PREDICT function, the output column name for the model is predicted_label_column_name.

The prediction is made against the optimal trial by default. You can select other trial by specifying trial_id parameter.

  ML.PREDICT(MODEL `bqml_tutorial.hp_taxi_tip_model`,
    STRUCT(3 AS trial_id))

For more details on how to use model serving functions, see Model serving functions.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

  • You can delete the project you created.
  • Or you can keep the project and delete the dataset.

Delete your dataset

Deleting your project removes all datasets and all tables in the project. If you prefer to reuse the project, you can delete the dataset you created in this tutorial:

  1. If necessary, open the BigQuery page in the Google Cloud console.

    Go to the BigQuery page

  2. In the navigation panel, click the bqml_tutorial dataset you created.

  3. On the right side of the window, click Delete dataset. This action deletes the dataset, the table, and all the data.

  4. In the Delete dataset dialog box, confirm the delete command by typing the name of your dataset (bqml_tutorial) and then click Delete.

Delete your project

To delete the project:

  1. In the Google Cloud console, go to the Manage resources page.

    Go to Manage resources

  2. In the project list, select the project that you want to delete, and then click Delete.
  3. In the dialog, type the project ID, and then click Shut down to delete the project.

What's next