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BigQuery ML mendukung jenis fitur input yang berbeda untuk jenis model yang berbeda.
Jenis fitur input yang didukung tercantum dalam tabel berikut:
BigQuery ML mendukung ARRAY<numerical> sebagai input vektor rapat selama pelatihan model. Fitur penyematan adalah jenis vektor padat khusus.
lihat fungsi ML.GENERATE_EMBEDDING untuk mengetahui informasi selengkapnya.
BigQuery ML mendukung ARRAY<STRUCT> sebagai input renggang selama pelatihan model. Setiap struktur berisi nilai INT64 yang mewakili
indeks berbasis nol, dan
jenis numerik
yang mewakili nilai yang sesuai.
Berikut adalah contoh input tensor renggang untuk array bilangan bulat
[0,1,0,0,0,0,1]:
ARRAY<STRUCT<k INT64, v INT64>>[(1, 1), (6, 1)] AS f1
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
Terakhir diperbarui pada 2025-08-17 UTC.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Informasi yang saya butuhkan tidak ada","missingTheInformationINeed","thumb-down"],["Terlalu rumit/langkahnya terlalu banyak","tooComplicatedTooManySteps","thumb-down"],["Sudah usang","outOfDate","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Masalah kode / contoh","samplesCodeIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 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"]]
