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Supported input feature typesBigQuery ML supports different input feature types for different model types. Supported input feature types are listed in the following table:
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. Dense vector inputBigQuery 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.
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
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Last updated 2025-08-07 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[[["BigQuery ML accommodates various input feature types, tailored to different model categories such as supervised, unsupervised, and time series models."],["Numeric, categorical, timestamp, struct, geography, and array types are supported across many BigQuery ML models, with specific models having certain specificities."],["Dense vector input is supported using `ARRAY\u003cnumerical\u003e` for model training, which includes a special embedding feature as seen in the `ML.GENERATE_EMBEDDING` function."],["Sparse input during model training is supported through the use of `ARRAY\u003cSTRUCT\u003e`, where each struct contains an `INT64` index and a numeric value."],["Matrix Factorization and ARIMA_PLUS models have unique input requirements, with the provided input types for ARIMA_PLUS_XREG only applying to external regressors."]]],[]]
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