In machine learning, hyperparameter tuning identifies a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a model argument whose value is set before the learning process begins. By contrast, the values of other parameters such as coefficients of a linear model are learned.
Hyperparameter tuning lets you spend less time manually iterating hyperparameters and more time focusing on exploring insights from data.
For information about the supported model types for each SQL statement and function, and for a list of all of the supported SQL statements and functions for each model type, read the End-to-end user journey for each model.
Hyperparameter Tuning Offerings in BigQuery ML
BigQuery ML supports hyperparameter tuning for the following machine learning models, including time series and non-time series models:
|Model Types||Hyperparameter tuning details||Syntax|
|Linear & logistic regression,Kmeans,Matrix factorization,Boosted trees,Random forest,DNN,Wide-and-Deep||This hyperparameter tuning feature is made possible in BigQuery ML by using Vertex AI Vizier.||See Hyperparameter tuning for CREATE MODEL statements and ML.TRIAL_INFO.|
|AutoML Tables models||Automatic hyperparameter tuning is embedded in the AutoML Tables model training by default.||See Create Model statement for AutoML Tables models.|
|ARIMA_PLUS||The auto.ARIMA algorithm performs hyperparameter tuning for the trend module. Hyperparameter tuning is not supported for the entire modeling pipeline. See the modeling pipeline for more details.||See CREATE MODEL statement for time-series models and ML.ARIMA_EVALUATE.|