您可以使用 CREATE MODEL 陳述式中的預設設定和推論函式，建立及使用 BigQuery ML 模型，即使沒有太多機器學習知識也能輕鬆上手。不過，如果具備機器學習開發生命週期的基本知識，例如特徵工程和模型訓練，有助於最佳化資料和模型，進而獲得更出色的結果。建議您使用下列資源，熟悉機器學習技術和程序：
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-09-09 (世界標準時間)。"],[[["\u003cp\u003eFeature preprocessing, encompassing both feature creation (engineering) and data cleaning, is a crucial step in the machine learning process.\u003c/p\u003e\n"],["\u003cp\u003eBigQuery ML offers automatic preprocessing during training, simplifying the process for users.\u003c/p\u003e\n"],["\u003cp\u003eManual preprocessing is also available in BigQuery ML, allowing for custom preprocessing definitions using the \u003ccode\u003eTRANSFORM\u003c/code\u003e clause and specific functions.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003eML.FEATURE_INFO\u003c/code\u003e function enables users to retrieve statistics about the input feature columns.\u003c/p\u003e\n"],["\u003cp\u003eBasic knowledge of the ML development lifecycle, including feature engineering and model training, is recommended for better optimization of data and models.\u003c/p\u003e\n"]]],[],null,["# Feature preprocessing overview\n==============================\n\n*Feature preprocessing* is one of the most important steps in the machine\nlearning lifecycle. It consists of creating features and cleaning the training\ndata. Creating features is also referred as *feature engineering*.\n\nBigQuery ML provides the following feature preprocessing techniques:\n\n- **Automatic preprocessing** . BigQuery ML performs automatic\n preprocessing during training. For more information, see [Automatic feature\n preprocessing](/bigquery/docs/reference/standard-sql/bigqueryml-auto-preprocessing).\n\n- **Manual preprocessing** . You can use the [`TRANSFORM` clause](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create#transform)\n in the `CREATE MODEL` statement to define custom preprocessing using [manual\n preprocessing\n functions](/bigquery/docs/manual-preprocessing#types_of_preprocessing_functions).\n You can also use these functions outside of the `TRANSFORM` clause to\n process training data before creating the model.\n\nGet feature information\n-----------------------\n\nYou can use the [`ML.FEATURE_INFO`\nfunction](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-feature) to\nretrieve the statistics of all input feature columns.\n\nRecommended knowledge\n---------------------\n\nBy using the default settings in the `CREATE MODEL` statements and the\ninference functions, you can create and use BigQuery ML models\neven without much ML knowledge. However, having basic knowledge about the\nML development lifecycle, such as feature engineering and model training,\nhelps you optimize both your data and your model to\ndeliver better results. We recommend using the following resources to develop\nfamiliarity with ML techniques and processes:\n\n- [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)\n- [Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning)\n- [Data Cleaning](https://www.kaggle.com/learn/data-cleaning)\n- [Feature Engineering](https://www.kaggle.com/learn/feature-engineering)\n- [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning)\n\nWhat's next\n-----------\n\nLearn about [feature serving](/bigquery/docs/feature-serving) in\nBigQuery ML."]]
