[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-05-06(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."]]],[]]