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> 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 ✔ ✔ ✔ ✔ ✔ ✔

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 ML.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<k INT64, v INT64>>[(1, 1), (6, 1)] AS f1