Regression overview

A common use case for machine learning is predicting the value of a numericalmetric 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 usingthe house's location and characteristics as features, you can compare this houseto similar houses that have already sold, and use their sales prices to estimatethe house's sale price.

You can use any of the following models in combination with theML.PREDICT functionto perform regression:

• Linear regression models:uselinear regressionby setting theMODEL_TYPE option toLINEAR_REG.
• Boosted tree models:use agradient boosted decision treeby setting theMODEL_TYPE option toBOOSTED_TREE_REGRESSOR.
• Random forest models:use arandom forestby setting theMODEL_TYPE option toRANDOM_FOREST_REGRESSOR.
• Deep neural network (DNN) models:use aneural networkby setting theMODEL_TYPE option toDNN_REGRESSOR.
• Wide & Deep models:usewide & deep learningby setting theMODEL_TYPE option toDNN_LINEAR_COMBINED_REGRESSOR.
• AutoML models:use anAutoML classification modelby setting theMODEL_TYPE option toAUTOML_REGRESSOR.

Recommended knowledge

By using the default settings in theCREATE MODEL statements and theML.PREDICT function, you can create and use a regression model evenwithout much ML knowledge. However, having basic knowledge aboutML development helps you optimize both your data and your model todeliver better results. We recommend using the following resources to developfamiliarity with ML techniques and processes:

• Machine Learning Crash Course
• Intro to Machine Learning
• Intermediate Machine Learning