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Descripción general del procesamiento previo de atributos
El procesamiento previo de atributos es uno de los pasos más importantes del ciclo de vida del aprendizaje automático. Consiste en crear atributos y limpiar los datos de entrenamiento. La creación de atributos también se conoce como ingeniería de atributos.
BigQuery ML proporciona las siguientes técnicas de procesamiento previo de atributos:
Procesamiento previo automático BigQuery ML realiza el procesamiento previo automático durante el entrenamiento. Para obtener más información, consulta Procesamiento previo automático de atributos.
Procesamiento previo manual: Puedes usar la cláusula TRANSFORM en la declaración CREATE MODEL para definir el procesamiento previo personalizado con las funciones de procesamiento previo manual.
También puedes usar estas funciones fuera de la cláusula TRANSFORM para procesar los datos de entrenamiento antes de crear el modelo.
Obtén información sobre los atributos
Puedes usar la función ML.FEATURE_INFO para recuperar las estadísticas de todas las columnas de atributos de entrada.
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2025-01-07 (UTC)"],[[["\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."]]
