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Ringkasan bobot model BigQuery ML
Dokumen ini menjelaskan cara BigQuery ML mendukung visibilitas bobot model untuk model machine learning (ML).
Model ML adalah artefak yang disimpan setelah menjalankan algoritma ML pada data pelatihan. Model ini mewakili aturan, angka, dan struktur data khusus algoritma lainnya yang diperlukan untuk membuat prediksi. Beberapa contoh termasuk berikut ini:
Model regresi linear terdiri dari vektor koefisien yang memiliki nilai tertentu.
Model pohon keputusan terdiri dari satu atau beberapa pohon pernyataan if-then yang memiliki nilai tertentu.
Model deep neural network terdiri dari struktur grafik dengan vektor atau matriks bobot yang memiliki nilai tertentu.
Dalam BigQuery ML, istilah bobot model digunakan untuk menjelaskan komponen yang menyusun sebuah model.
Mengambil koefisien model ARIMA, yang digunakan untuk membuat model komponen tren deret waktu input. Untuk informasi tentang komponen lainnya, seperti pola musiman yang ada dalam deret waktu, gunakan ML.ARIMA_EVALUATE.
BigQuery ML tidak mendukung fungsi bobot model untuk jenis model berikut:
Untuk melihat bobot semua jenis model ini kecuali untuk model AutoML Tables, ekspor model dari BigQuery ML ke Cloud Storage.
Kemudian Anda dapat menggunakan library XGBoost untuk memvisualisasikan struktur pohon untuk model hutan acak dan pohon yang ditingkatkan, atau library TensorFlow untuk memvisualisasikan struktur grafik model DNN dan wide-and-deep. Tidak ada metode untuk mendapatkan informasi bobot model bagi model AutoML Tables.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-17 UTC."],[[["\u003cp\u003eBigQuery ML uses the term "model weights" to describe the components that make up a machine learning model, such as coefficients, trees of if-then statements, or graph structures with weights.\u003c/p\u003e\n"],["\u003cp\u003eBigQuery ML provides functions like \u003ccode\u003eML.WEIGHTS\u003c/code\u003e, \u003ccode\u003eML.CENTROIDS\u003c/code\u003e, \u003ccode\u003eML.PRINCIPAL_COMPONENTS\u003c/code\u003e, \u003ccode\u003eML.PRINCIPAL_COMPONENT_INFO\u003c/code\u003e, and \u003ccode\u003eML.ARIMA_COEFFICIENTS\u003c/code\u003e to retrieve model weights for various supervised and unsupervised model types.\u003c/p\u003e\n"],["\u003cp\u003eSupported model categories include supervised models like Linear and Logistic Regression, and unsupervised models like Kmeans, Matrix Factorization, and PCA, alongside Time series models such as ARIMA_PLUS, each having their corresponding weight retrieval functions.\u003c/p\u003e\n"],["\u003cp\u003eModel weight functions are not supported for models like Boosted tree, Random forest, Deep neural network (DNN), Wide-and-deep, and AutoML Tables, however, you can export most of these model types to Cloud Storage to visualize them using XGBoost or TensorFlow, except for AutoML Tables.\u003c/p\u003e\n"]]],[],null,["# BigQuery ML model weights overview\n==================================\n\nThis document describes how BigQuery ML supports model weights\ndiscoverability for machine learning (ML) models.\n\nAn ML model is an artifact that is saved after running an ML algorithm on\ntraining data. The model represents the rules, numbers,\nand any other algorithm-specific data structures that are required to make\npredictions. Some examples include the following:\n\n- A linear regression model is comprised of a vector of coefficients that have specific values.\n- A decision tree model is comprised of one or more trees of if-then statements that have specific values.\n- A deep neural network model is comprised of a graph structure with vectors or matrices of weights that have specific values.\n\nIn BigQuery ML, the term *model weights* is used to describe the\ncomponents that a model is comprised of.\n\nFor information about the supported SQL statements and functions for each\nmodel type, see\n[End-to-end user journey for each model](/bigquery/docs/e2e-journey).\n\nModel weights offerings in BigQuery ML\n--------------------------------------\n\nBigQuery ML offers multiple functions that you can use to\nretrieve the model weights for different models.\n\nBigQuery ML doesn't support model weight functions for the\nfollowing types of models:\n\n- [Boosted tree](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-boosted-tree)\n- [Random forest](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-random-forest)\n- [Deep neural network (DNN)](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-dnn-models)\n- [Wide-and-deep](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models)\n- [AutoML Tables](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-automl)\n\nTo see the weights of all of these model types except for AutoML Tables\nmodels, export the model from BigQuery ML to Cloud Storage.\nYou can then use the XGBoost library to visualize the tree structure for\nboosted tree and random forest models, or the TensorFlow library\nto visualize the graph structure for DNN and wide-and-deep models. There is no\nmethod for getting model weight information for AutoML Tables models.\n\nFor more information about exporting a model, see\n[`EXPORT MODEL` statement](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-export-model)\nand\n[Export a BigQuery ML model for online prediction](/bigquery/docs/export-model-tutorial)."]]
