CREATE MODEL 문과 ML.PREDICT 함수의 기본 설정을 사용하면 많은 ML 지식 없이도 분류 모델을 만들고 사용할 수 있습니다. 하지만 ML 개발에 관한 기본 지식을 알고 있으면 데이터와 모델을 모두 최적화하여 더 나은 결과를 얻을 수 있습니다. ML 기법과 프로세스에 익숙해지려면 다음 리소스를 사용하는 것이 좋습니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-07-14(UTC)"],[[["Machine learning classification involves using a model trained on labeled data to classify new data, such as identifying spam emails or categorizing customer reviews."],["The `ML.PREDICT` function can be used with various classification models, including logistic regression, boosted tree, random forest, deep neural network (DNN), wide & deep, and AutoML models."],["Different models can be specified using the `MODEL_TYPE` option, such as `LOGISTIC_REG`, `BOOSTED_TREE_CLASSIFIER`, `RANDOM_FOREST_CLASSIFIER`, `DNN_CLASSIFIER`, `DNN_LINEAR_COMBINED_CLASSIFIER`, and `AUTOML_CLASSIFIER`."],["While 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."],["Resources like the Machine Learning Crash Course, Intro to Machine Learning, and Intermediate Machine Learning are recommended for gaining familiarity with machine learning techniques."]]],[]]
