[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-03-06。"],[[["BigQuery 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."],["BigQuery ML provides functions like `ML.WEIGHTS`, `ML.CENTROIDS`, `ML.PRINCIPAL_COMPONENTS`, `ML.PRINCIPAL_COMPONENT_INFO`, and `ML.ARIMA_COEFFICIENTS` to retrieve model weights for various supervised and unsupervised model types."],["Supported 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."],["Model 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."]]],[]]