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    Perform classification with a boosted trees model

    This tutorial teaches you how to use a boosted trees classifier model to predict the income range of individuals based on their demographic data. The model predicts whether a value falls into one of two categories, in this case whether an individual's annual income falls above or below $50,000.

    This tutorial uses the bigquery-public-data.ml_datasets.census_adult_income dataset. This dataset contains the demographic and income information of US residents from 2000 and 2010.

    Objectives

    This tutorial guides you through completing the following tasks:

    Costs

    This tutorial uses billable components of Google Cloud, including the following:

    • BigQuery
    • BigQuery ML

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

    For more information about BigQuery ML costs, see BigQuery ML pricing .

    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.

      Roles required to select or create a project

      • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
      • Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

      Go to project selector

    3. Verify 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.

      Roles required to select or create a project

      • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
      • Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

      Go to project selector

    5. Verify that billing is enabled for your Google Cloud project .

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

      Enable the BigQuery API.

      Roles required to enable APIs

      To enable APIs, you need the Service Usage Admin IAM role ( roles/serviceusage.serviceUsageAdmin ), which contains the serviceusage.services.enable permission. Learn how to grant roles .

      Enable the API

    Required Permissions

    • To create the dataset, you need the bigquery.datasets.create IAM permission.

    • To create the model, you need the following permissions:

      • bigquery.jobs.create
      • bigquery.models.create
      • bigquery.models.getData
      • bigquery.models.updateData

    • To run inference, you need the following permissions:

      • bigquery.models.getData
      • bigquery.jobs.create

    For more information about IAM roles and permissions in BigQuery, see Introduction to IAM .

    Create a dataset

    Create a BigQuery dataset to store your ML model.

    Console

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to the BigQuery page

    2. In the Explorerpane, click your project name.

    3. Click View actions > Create dataset

    4. On the Create datasetpage, do the following:

      • For Dataset ID, enter bqml_tutorial .

      • For Location type, select Multi-region, and then select US.

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

    bq

    To create a new dataset, use the bq mk --dataset command .

    1. Create a dataset named bqml_tutorial with the data location set to US .

      bq mk --dataset \
  --location=US \
  --description "BigQuery ML tutorial dataset." \
  bqml_tutorial

    2. Confirm that the dataset was created:

      bq  
ls

    API

    Call the datasets.insert method with a defined dataset resource .

     { 
  
 "datasetReference" 
 : 
  
 { 
  
 "datasetId" 
 : 
  
 "bqml_tutorial" 
  
 } 
 }

    BigQuery DataFrames

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      import 
  
 google.cloud.bigquery 
 bqclient 
 = 
 google 
 . 
 cloud 
 . 
  bigquery 
 
 . 
  Client 
 
 () 
 bqclient 
 . 
  create_dataset 
 
 ( 
 "bqml_tutorial" 
 , 
 exists_ok 
 = 
 True 
 )

    Prepare the sample data

    The model you create in this tutorial predicts the income bracket for census respondents, based on the following features:

    • Age
    • Type of work performed
    • Marital status
    • Level of education
    • Occupation
    • Hours worked per week

    The education column isn't included in the training data, because the education and education_num columns both express the respondent's level of education in different formats.

    You separate the data into training, evaluation, and prediction sets by creating a new dataframe column that is derived from the functional_weight column. Eighty percent of the data is used for training the model, and the remaining twenty percent of the data is used for evaluation and prediction.

    SQL

    To prepare your sample data, create a view to contain the training data. This view is used by the CREATE MODEL statement later in this tutorial.

    Run the query that prepares the sample data:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, run the following query:

       CREATE 
  
 OR 
  
 REPLACE 
  
 VIEW 
  
 `bqml_tutorial.input_data` 
  
 AS 
 SELECT 
  
 age 
 , 
  
 workclass 
 , 
  
 marital_status 
 , 
  
 education_num 
 , 
  
 occupation 
 , 
  
 hours_per_week 
 , 
  
 income_bracket 
 , 
  
 CASE 
  
 WHEN 
  
 MOD 
 ( 
 functional_weight 
 , 
  
 10 
 ) 
  
 < 
  
 8 
  
 THEN 
  
 'training' 
  
 WHEN 
  
 MOD 
 ( 
 functional_weight 
 , 
  
 10 
 ) 
  
 = 
  
 8 
  
 THEN 
  
 'evaluation' 
  
 WHEN 
  
 MOD 
 ( 
 functional_weight 
 , 
  
 10 
 ) 
  
 = 
  
 9 
  
 THEN 
  
 'prediction' 
  
 END 
  
 AS 
  
 dataframe 
 FROM 
  
 `bigquery-public-data.ml_datasets.census_adult_income` 
 ;

    3. In the left pane, click Explorer:

      Highlighted button for the Explorer pane.

      If you don't see the left pane, click Expand left paneto open the pane.

    4. In the Explorerpane, search for the bqml_tutorial dataset.

    5. Click the dataset, and then click Overview > Tables.

    6. Click the input_data view to open the information pane. The view schema appears in the Schematab.

    BigQuery DataFrames

    Create a DataFrame called input_data . You use input_data later in this tutorial to use to train the model, evaluate it, and make predictions.

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 input_data 
 = 
 bpd 
 . 
 read_gbq 
 ( 
 "bigquery-public-data.ml_datasets.census_adult_income" 
 , 
 columns 
 = 
 ( 
 "age" 
 , 
 "workclass" 
 , 
 "marital_status" 
 , 
 "education_num" 
 , 
 "occupation" 
 , 
 "hours_per_week" 
 , 
 "income_bracket" 
 , 
 "functional_weight" 
 , 
 ), 
 ) 
 input_data 
 [ 
 "dataframe" 
 ] 
 = 
 bpd 
 . 
 Series 
 ( 
 "training" 
 , 
 index 
 = 
 input_data 
 . 
 index 
 ,) 
 . 
 case_when 
 ( 
 [ 
 ((( 
 input_data 
 [ 
 "functional_weight" 
 ] 
 % 
 10 
 ) 
 == 
 8 
 ), 
 "evaluation" 
 ), 
 ((( 
 input_data 
 [ 
 "functional_weight" 
 ] 
 % 
 10 
 ) 
 == 
 9 
 ), 
 "prediction" 
 ), 
 ] 
 ) 
 del 
 input_data 
 [ 
 "functional_weight" 
 ]

    Create the boosted trees model

    Create a boosted trees model to predict census respondents' income bracket, and train it on the census data. The query takes about 30 minutes to complete.

    SQL

    Follow these steps to create the model:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, paste in the following query and click Run:

       CREATE 
  
 MODEL 
  
 `bqml_tutorial.tree_model` 
 OPTIONS 
 ( 
 MODEL_TYPE 
 = 
 'BOOSTED_TREE_CLASSIFIER' 
 , 
  
 BOOSTER_TYPE 
  
 = 
  
 'GBTREE' 
 , 
  
 NUM_PARALLEL_TREE 
  
 = 
  
 1 
 , 
  
 MAX_ITERATIONS 
  
 = 
  
 50 
 , 
  
 TREE_METHOD 
  
 = 
  
 'HIST' 
 , 
  
 EARLY_STOP 
  
 = 
  
 FALSE 
 , 
  
 SUBSAMPLE 
  
 = 
  
 0.85 
 , 
  
 INPUT_LABEL_COLS 
  
 = 
  
 [ 
 'income_bracket' 
 ] 
 ) 
 AS 
  
 SELECT 
  
 * 
  
 EXCEPT 
 ( 
 dataframe 
 ) 
 FROM 
  
 `bqml_tutorial.input_data` 
 WHERE 
  
 dataframe 
  
 = 
  
 'training' 
 ;

      After the query completes, the tree_model model can be accessed through the Explorerpane. Because the query uses a CREATE MODEL statement to create a model, you don't see query results.

    BigQuery DataFrames

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      from 
  
 bigframes.ml 
  
 import 
 ensemble 
 # input_data is defined in an earlier step. 
 training_data 
 = 
 input_data 
 [ 
 input_data 
 [ 
 "dataframe" 
 ] 
 == 
 "training" 
 ] 
 X 
 = 
 training_data 
 . 
 drop 
 ( 
 columns 
 = 
 [ 
 "income_bracket" 
 , 
 "dataframe" 
 ]) 
 y 
 = 
 training_data 
 [ 
 "income_bracket" 
 ] 
 # create and train the model 
 tree_model 
 = 
 ensemble 
 . 
 XGBClassifier 
 ( 
 n_estimators 
 = 
 1 
 , 
 booster 
 = 
 "gbtree" 
 , 
 tree_method 
 = 
 "hist" 
 , 
 max_iterations 
 = 
 1 
 , 
 # For a more accurate model, try 50 iterations. 
 subsample 
 = 
 0.85 
 , 
 ) 
 tree_model 
 . 
 fit 
 ( 
 X 
 , 
 y 
 ) 
 tree_model 
 . 
 to_gbq 
 ( 
 your_model_id 
 , 
 # For example: "your-project.bqml_tutorial.tree_model" 
 replace 
 = 
 True 
 , 
 )

    Evaluate the model

    SQL

    Follow these steps to evaluate the model:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, paste in the following query and click Run:

        
 SELECT 
  
 * 
  
 FROM 
  
 ML 
 . 
 EVALUATE 
  
 ( 
 MODEL 
  
 `bqml_tutorial.tree_model` 
 , 
  
 ( 
  
 SELECT 
  
 * 
  
 FROM 
  
 `bqml_tutorial.input_data` 
  
 WHERE 
  
 dataframe 
  
 = 
  
 'evaluation' 
  
 ) 
  
 );

      The results should look similar to the following:

      +---------------------+---------------------+---------------------+-------------------+---------------------+---------------------+
| precision           | recall              | accuracy            | f1_score          | log_loss            | roc_auc             |
+---------------------+---------------------+---------------------+-------------------+-------------------------------------------+
| 0.67192429022082023 | 0.57880434782608692 | 0.83942963422194672 | 0.621897810218978 | 0.34405456040833338 | 0.88733566433566435 |
+---------------------+---------------------+ --------------------+-------------------+---------------------+---------------------+

    BigQuery DataFrames

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      # Select model you'll use for predictions. `read_gbq_model` loads model 
 # data from BigQuery, but you could also use the `tree_model` object 
 # from the previous step. 
 tree_model 
 = 
 bpd 
 . 
 read_gbq_model 
 ( 
 your_model_id 
 , 
 # For example: "your-project.bqml_tutorial.tree_model" 
 ) 
 # input_data is defined in an earlier step. 
 evaluation_data 
 = 
 input_data 
 [ 
 input_data 
 [ 
 "dataframe" 
 ] 
 == 
 "evaluation" 
 ] 
 X 
 = 
 evaluation_data 
 . 
 drop 
 ( 
 columns 
 = 
 [ 
 "income_bracket" 
 , 
 "dataframe" 
 ]) 
 y 
 = 
 evaluation_data 
 [ 
 "income_bracket" 
 ] 
 # The score() method evaluates how the model performs compared to the 
 # actual data. Output DataFrame matches that of ML.EVALUATE(). 
 score 
 = 
 tree_model 
 . 
 score 
 ( 
 X 
 , 
 y 
 ) 
 score 
 . 
 peek 
 () 
 # Output: 
 #    precision    recall  accuracy  f1_score  log_loss   roc_auc 
 # 0   0.671924  0.578804  0.839429  0.621897  0.344054  0.887335

    The evaluation metrics indicate good model performance, in particular, the fact that the roc_auc score is greater than 0.8 .

    For more information about the evaluation metrics, see Output .

    Use the model to predict classifications

    SQL

    Follow these steps to forecast data with the model:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, paste in the following query and click Run:

        
 SELECT 
  
 * 
  
 FROM 
  
 ML 
 . 
 PREDICT 
  
 ( 
 MODEL 
  
 `bqml_tutorial.tree_model` 
 , 
  
 ( 
  
 SELECT 
  
 * 
  
 FROM 
  
 `bqml_tutorial.input_data` 
  
 WHERE 
  
 dataframe 
  
 = 
  
 'prediction' 
  
 ) 
  
 );

    The first few columns of the results should look similar to the following:

    +---------------------------+--------------------------------------+-------------------------------------+
  | predicted_income_bracket  | predicted_income_bracket_probs.label | predicted_income_bracket_probs.prob |
  +---------------------------+--------------------------------------+-------------------------------------+
  |  <=50K                    |  >50K                                | 0.05183430016040802                 |
  +---------------------------+--------------------------------------+-------------------------------------+
  |                           |  <50K                                | 0.94816571474075317                 |
  +---------------------------+--------------------------------------+-------------------------------------+
  |  <=50K                    |  >50K                                | 0.00365859130397439                 |
  +---------------------------+--------------------------------------+-------------------------------------+
  |                           |  <50K                                | 0.99634140729904175                 |
  +---------------------------+--------------------------------------+-------------------------------------+
  |  <=50K                    |  >50K                                | 0.037775970995426178                |
  +---------------------------+--------------------------------------+-------------------------------------+
  |                           |  <50K                                | 0.96222406625747681                 |
  +---------------------------+--------------------------------------+-------------------------------------+

    BigQuery DataFrames

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      # Select model you'll use for predictions. `read_gbq_model` loads model 
 # data from BigQuery, but you could also use the `tree_model` object 
 # from previous steps. 
 tree_model 
 = 
 bpd 
 . 
 read_gbq_model 
 ( 
 your_model_id 
 , 
 # For example: "your-project.bqml_tutorial.tree_model" 
 ) 
 # input_data is defined in an earlier step. 
 prediction_data 
 = 
 input_data 
 [ 
 input_data 
 [ 
 "dataframe" 
 ] 
 == 
 "prediction" 
 ] 
 predictions 
 = 
 tree_model 
 . 
 predict 
 ( 
 prediction_data 
 ) 
 predictions 
 . 
 peek 
 () 
 # Output: 
 # predicted_income_bracket   predicted_income_bracket_probs.label  predicted_income_bracket_probs.prob 
 #                   <=50K                                   >50K                   0.05183430016040802 
 #                                                           <50K                   0.94816571474075317 
 #                   <=50K                                   >50K                   0.00365859130397439 
 #                                                           <50K                   0.99634140729904175 
 #                   <=50K                                   >50K                   0.037775970995426178 
 #                                                           <50K                   0.96222406625747681

    The predicted_income_bracket contains the predicted value from the model. The predicted_income_bracket_probs.label shows the two labels that the model had to choose between, and the predicted_income_bracket_probs.prob column shows the probability of the given label being the correct one.

    For more information about the output columns, see Classification models .

    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, click the bqml_tutorialdataset you created.

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

    4. In the Delete datasetdialog, 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.

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