Regression overview

A common use case for machine learning is predicting the value of a numerical metric for new data by using a model trained on similar historical data. For example, you might want to predict a house's expected sale price. By using the house's location and characteristics as features, you can compare this house to similar houses that have already sold, and use their sales prices to estimate the house's sale price.

You can use any of the following models in BigQuery ML to perform regression:

• Linear regression models: use linear regression by setting the MODEL_TYPE option to LINEAR_REG.
• Boosted tree models: use a gradient boosted decision tree by setting the MODEL_TYPE option to BOOSTED_TREE_REGRESSOR.
• Random forest models: use a random forest by setting the MODEL_TYPE option to RANDOM_FOREST_REGRESSOR.
• Deep neural network (DNN) models: use a neural network by setting the MODEL_TYPE option to DNN_REGRESSOR.
• Wide & Deep models: use wide & deep learning by setting the MODEL_TYPE option to DNN_LINEAR_COMBINED_REGRESSOR.
• AutoML models: use an AutoML classification model by setting the MODEL_TYPE option to AUTOML_REGRESSOR.