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분류 개요
머신러닝의 일반적인 사용 사례는 유사한 라벨이 지정된 데이터로 학습된 모델을 사용하여 새 데이터를 분류하는 것입니다. 예를 들어 이메일이 스팸인지 또는 고객 제품 리뷰가 긍정적인지, 부정적인지, 중립적인지 예측할 수 있습니다.
ML.PREDICT 함수와 함께 다음 모델을 사용하여 분류를 실행할 수 있습니다.
추천 지식
CREATE MODEL 문의 기본 설정과 ML.PREDICT 함수를 사용하면 ML 지식이 많지 않더라도 분류 모델을 만들고 사용할 수 있습니다. 하지만 ML 개발에 관한 기본 지식을 갖추면 데이터와 모델을 모두 최적화하여 더 나은 결과를 얻을 수 있습니다. 다음 리소스를 사용하여 ML 기법과 프로세스에 익숙해지는 것이 좋습니다.
달리 명시되지 않는 한 이 페이지의 콘텐츠에는 Creative Commons Attribution 4.0 라이선스에 따라 라이선스가 부여되며, 코드 샘플에는 Apache 2.0 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 Google Developers 사이트 정책을 참조하세요. 자바는 Oracle 및/또는 Oracle 계열사의 등록 상표입니다.
최종 업데이트: 2025-08-19(UTC)
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["필요한 정보가 없음","missingTheInformationINeed","thumb-down"],["너무 복잡함/단계 수가 너무 많음","tooComplicatedTooManySteps","thumb-down"],["오래됨","outOfDate","thumb-down"],["번역 문제","translationIssue","thumb-down"],["샘플/코드 문제","samplesCodeIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-08-19(UTC)"],[[["\u003cp\u003eMachine learning classification involves using a model trained on labeled data to classify new data, such as identifying spam emails or categorizing customer reviews.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003eML.PREDICT\u003c/code\u003e function can be used with various classification models, including logistic regression, boosted tree, random forest, deep neural network (DNN), wide & deep, and AutoML models.\u003c/p\u003e\n"],["\u003cp\u003eDifferent models can be specified using the \u003ccode\u003eMODEL_TYPE\u003c/code\u003e option, such as \u003ccode\u003eLOGISTIC_REG\u003c/code\u003e, \u003ccode\u003eBOOSTED_TREE_CLASSIFIER\u003c/code\u003e, \u003ccode\u003eRANDOM_FOREST_CLASSIFIER\u003c/code\u003e, \u003ccode\u003eDNN_CLASSIFIER\u003c/code\u003e, \u003ccode\u003eDNN_LINEAR_COMBINED_CLASSIFIER\u003c/code\u003e, and \u003ccode\u003eAUTOML_CLASSIFIER\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eWhile classification models can be created and used without extensive ML knowledge, understanding the basics can help optimize both data and the model for better results.\u003c/p\u003e\n"],["\u003cp\u003eResources like the Machine Learning Crash Course, Intro to Machine Learning, and Intermediate Machine Learning are recommended for gaining familiarity with machine learning techniques.\u003c/p\u003e\n"]]],[],null,["# Classification overview\n=======================\n\nA common use case for machine learning is classifying new data by using a model\ntrained on similar labeled data. For example, you might want to predict whether\nan email is spam, or whether a customer product review is positive, negative, or\nneutral.\n\nYou can use any of the following models in combination with the\n[`ML.PREDICT` function](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict)\nto perform classification:\n\n- [Logistic regression models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm): use [logistic regression](https://developers.google.com/machine-learning/crash-course/logistic-regression) by setting the `MODEL_TYPE` option to `LOGISTIC_REG`.\n- [Boosted tree models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-boosted-tree): use a [gradient boosted decision tree](https://developers.google.com/machine-learning/decision-forests/intro-to-gbdt) by setting the `MODEL_TYPE` option to `BOOSTED_TREE_CLASSIFIER`.\n- [Random forest models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-random-forest): use a [random forest](https://developers.google.com/machine-learning/decision-forests/intro-to-decision-forests) by setting the `MODEL_TYPE` option to `RANDOM_FOREST_CLASSIFIER`.\n- [Deep neural network (DNN) models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-dnn-models): use a [neural network](https://developers.google.com/machine-learning/crash-course/neural-networks) by setting the `MODEL_TYPE` option to `DNN_CLASSIFIER`.\n- [Wide \\& Deep models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models): use [wide \\& deep learning](https://dl.acm.org/doi/10.1145/2988450.2988454) by setting the `MODEL_TYPE` option to `DNN_LINEAR_COMBINED_CLASSIFIER`.\n- [AutoML models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-automl): use an [AutoML classification model](/vertex-ai/docs/tabular-data/classification-regression/overview) by setting the `MODEL_TYPE` option to `AUTOML_CLASSIFIER`.\n\nRecommended knowledge\n---------------------\n\nBy using the default settings in the `CREATE MODEL` statements and the\n`ML.PREDICT` function, you can create and use a classification model even\nwithout much ML knowledge. However, having basic knowledge about\nML development helps 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- [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning)"]]
