Train the model

1. In the Amazon SageMaker AI console, select Model training & customization -> Training & tuning jobs -> Training jobs from the left menu, and then select Create training job:

2. In the Create training job window, enter a job name, such as xgboost-bank. For the IAM role, select an existing SageMaker execution role with access to the Amazon S3 bucket, or select Create a new role. When creating a new role, choose Any S3 bucket, and then select Create role:

3. For the algorithm, select Tabular – XGBoost : v1.3:

4. Under Resource configuration, use the following values:

   • Instance type: ml.m4.xlarge
   • Instance count: 1
   • Storage volume: 30 GB
   • Maximum runtime: 1 hour
   • Keep alive period: 0 seconds

   This is a short training job and should typically complete within 10 minutes.

5. Enter the following hyperparameters and leave the remaining settings at their default values:

   num_round=100
   verbosity=1
   eta=0.2
   gamma=4
   max_depth=5
   min_child_weight=6
   subsample=0.8
   objective=binary:logistic
   

6. Under Input data configuration, use the following values:

   • Channel name: train
   • Input mode: File
   • S3 data type: S3Prefix
   • Distribution: FullyReplicated
   • Data format: csv
   • S3 location: The Amazon S3 prefix containing train.csv

7. Under Output data configuration, enter the Amazon S3 location where Amazon SageMaker AI will store the model artifacts:

1. Leave the remaining settings at their default values and select Create training job. For detailed instructions on configuring a training job, see the Amazon SageMaker AI Developer Guide.

After the training job is created, Amazon SageMaker AI launches the ML instance and trains the model. When the training job is complete, Amazon SageMaker AI stores the resulting model artifact in the configured Amazon S3 output location:

<output-path>/<training-job-name>/output/model.tar.gz