BigQuery DataFrames로 클러스터링 모델 만들기
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BigQuery DataFrames API를 사용하여 펭귄의 길이와 성별에 대한 k-평균 클러스터링 모델을 만듭니다.
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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.
[[["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"]],[],[[["\u003cp\u003eThis content demonstrates creating a k-means clustering model using the BigQuery DataFrames API.\u003c/p\u003e\n"],["\u003cp\u003eThe model is trained on penguin data, specifically using culmen length and sex as features.\u003c/p\u003e\n"],["\u003cp\u003eThe example code showcases how to load data from BigQuery, create and fit the KMeans model, and then predict and score it.\u003c/p\u003e\n"],["\u003cp\u003eYou can find more detailed documentation on using BigQuery DataFrames, setting up authentication, and using the BigQuery Python API in the provided links.\u003c/p\u003e\n"]]],[],null,["# Create a clustering model with BigQuery DataFrames\n\nCreate a k-means clustering model on the lengths and sex of penguins using the BigQuery DataFrames API.\n\nExplore further\n---------------\n\n\nFor detailed documentation that includes this code sample, see the following:\n\n- [Use BigQuery DataFrames](/bigquery/docs/use-bigquery-dataframes)\n\nCode sample\n-----------\n\n### Python\n\n\nBefore trying this sample, follow the Python setup instructions in the\n[BigQuery quickstart using\nclient libraries](/bigquery/docs/quickstarts/quickstart-client-libraries).\n\n\nFor more information, see the\n[BigQuery Python API\nreference documentation](/python/docs/reference/bigquery/latest).\n\n\nTo authenticate to BigQuery, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for client libraries](/bigquery/docs/authentication#client-libs).\n\n from bigframes.ml.cluster import KMeans\n import bigframes.pandas as bpd\n\n # Load data from BigQuery\n query_or_table = \"bigquery-public-data.ml_datasets.penguins\"\n bq_df = bpd.read_gbq(query_or_table)\n\n # Create the KMeans model\n cluster_model = KMeans(n_clusters=10)\n cluster_model.fit(bq_df[\"culmen_length_mm\"], bq_df[\"sex\"])\n\n # Predict using the model\n result = cluster_model.predict(bq_df)\n # Score the model\n score = cluster_model.score(bq_df)\n\nWhat's next\n-----------\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=bigquery)."]]
