Supported input feature types

BigQuery ML supports different input feature types for different model types. Supported input feature types are listed in the following table:

Model Category	Model Types	Numeric types (INT64, NUMERIC, BIGNUMERIC, FLOAT64)	Categorical types (BOOL, STRING, BYTES, DATE, DATETIME)	TIMESTAMP	STRUCT	GEOGRAPHY	ARRAY<Numeric types>	ARRAY<Categorical types>
Supervised Learning	Linear & Logistic Regression	✔	✔	✔	✔	✔	✔	✔
	Deep Neural Networks	✔	✔	✔	✔	✔	✔
	Wide-and-Deep	✔	✔	✔	✔	✔	✔
	Boosted trees	✔	✔	✔	✔	✔	✔
	AutoML Tables	✔	✔	✔	✔	✔	✔
Unsupervised Learning	K-means	✔	✔	✔	✔	✔	✔	✔
	PCA	✔	✔	✔	✔	✔	✔
	Autoencoder	✔	✔	✔	✔	✔	✔	✔
Time Series Models	ARIMA_PLUS_XREG	✔	✔	✔	✔	✔	✔

Dense vector input

BigQuery ML supports ARRAY<numerical> as dense vector input during model training. The embedding feature is a special type of dense vector. see the AI.GENERATE_EMBEDDING function for more information.

Sparse input

BigQuery ML supports ARRAY<STRUCT> as sparse input during model training. Each struct contains an INT64 value that represents its zero-based index, and a numeric type that represents the corresponding value.

Below is an example of a sparse tensor input for the integer array [0,1,0,0,0,0,1]:

ARRAY<STRUCT<kINT64,vINT64>>[(1,1),(6,1)]ASf1