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