Supported input feature types

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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
Boosted Trees
AutoML Tables
Unsupervised Learning K-means
Time Series Models ARIMA_PLUS_XREG

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