이 페이지의 일부 또는 모든 정보는 Trusted Cloud by S3NS에 적용되지 않을 수 있습니다.
BigQuery ML은 여러 모델 유형에 대해 서로 다른 입력 특성 유형을 지원합니다.
다음 표는 지원되는 입력 특성 유형을 보여줍니다.
BigQuery ML은 ARRAY<numerical>를 모델 학습 중 밀집 벡터 입력으로 지원합니다. 임베딩 특성은 특수한 유형의 밀집 벡터입니다. 자세한 내용은 ML.GENERATE_EMBEDDING 함수를 참조하세요.
BigQuery ML은 ARRAY<STRUCT>를 모델 학습 중 희소 입력으로 지원합니다. 각 구조체에는 해당 0 기반 색인을 나타내는 INT64 값과 해당 값을 나타내는 숫자 유형이 포함됩니다.
다음은 정수 배열 [0,1,0,0,0,0,1]의 희소 텐서 입력 예시입니다.
ARRAY<STRUCT<k INT64, v INT64>>[(1, 1), (6, 1)] AS f1
달리 명시되지 않는 한 이 페이지의 콘텐츠에는 Creative Commons Attribution 4.0 라이선스에 따라 라이선스가 부여되며, 코드 샘플에는 Apache 2.0 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 Google Developers 사이트 정책을 참조하세요. 자바는 Oracle 및/또는 Oracle 계열사의 등록 상표입니다.
최종 업데이트: 2025-08-17(UTC)
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["필요한 정보가 없음","missingTheInformationINeed","thumb-down"],["너무 복잡함/단계 수가 너무 많음","tooComplicatedTooManySteps","thumb-down"],["오래됨","outOfDate","thumb-down"],["번역 문제","translationIssue","thumb-down"],["샘플/코드 문제","samplesCodeIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-08-17(UTC)"],[[["\u003cp\u003eBigQuery ML accommodates various input feature types, tailored to different model categories such as supervised, unsupervised, and time series models.\u003c/p\u003e\n"],["\u003cp\u003eNumeric, categorical, timestamp, struct, geography, and array types are supported across many BigQuery ML models, with specific models having certain specificities.\u003c/p\u003e\n"],["\u003cp\u003eDense vector input is supported using \u003ccode\u003eARRAY<numerical>\u003c/code\u003e for model training, which includes a special embedding feature as seen in the \u003ccode\u003eML.GENERATE_EMBEDDING\u003c/code\u003e function.\u003c/p\u003e\n"],["\u003cp\u003eSparse input during model training is supported through the use of \u003ccode\u003eARRAY<STRUCT>\u003c/code\u003e, where each struct contains an \u003ccode\u003eINT64\u003c/code\u003e index and a numeric value.\u003c/p\u003e\n"],["\u003cp\u003eMatrix Factorization and ARIMA_PLUS models have unique input requirements, with the provided input types for ARIMA_PLUS_XREG only applying to external regressors.\u003c/p\u003e\n"]]],[],null,["# Supported input feature types\n=============================\n\nBigQuery ML supports different input feature types for different model types.\nSupported input feature types are listed in the following table:\n\n| **Note:** [Matrix Factorization](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-matrix-factorization#inputs) and [ARIMA_PLUS](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#time_series_data_col) models have special input feature types. The input types listed for [ARIMA_PLUS_XREG](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-multivariate-time-series#time_series_data_col) are only for external regressors.\n\nDense vector input\n------------------\n\nBigQuery ML supports `ARRAY\u003cnumerical\u003e` as dense vector input\nduring model training. The embedding feature is a special type of dense vector.\nsee the [`ML.GENERATE_EMBEDDING` function](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-embedding) for more information.\n\nSparse input\n------------\n\nBigQuery ML supports `ARRAY\u003cSTRUCT\u003e` as sparse input during\nmodel training. Each struct contains an `INT64` value that represents its\nzero-based index, and a\n[numeric type](/bigquery/docs/reference/standard-sql/data-types#numeric_types)\nthat represents the corresponding value.\n\nBelow is an example of a sparse tensor input for the integer array\n`[0,1,0,0,0,0,1]`: \n\n ARRAY\u003cSTRUCT\u003ck INT64, v INT64\u003e\u003e[(1, 1), (6, 1)] AS f1"]]
