Supported input feature types

BigQuery ML supports different input feature types for different model types. You can find the supported input feature types in the table below.

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>	ARRAY<STRUCT<INT64, Numeric 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	✔	✔	✔	✔			✔	✔

Note: Matrix Factorization and ARIMA_PLUS models have special input feature types. The input types listed for ARIMA_PLUS_XREG are only for external regressors.

Support sparse input for feature columns

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 types that represents the corresponding value.

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

ARRAY<STRUCT<k INT64, v INT64>>[(1, 1), (6, 1)] AS f1