貢献度分析（主要因分析）を使用すると、多次元データの主な指標の変化について分析情報を生成できます。たとえば、貢献度分析を使用して、2 つの四半期にわたる収益額の変化を確認したり、2 つのトレーニング データセットを比較して ML モデルのパフォーマンスの変化を把握したりできます。BigQuery で貢献度分析モデルを作成するには、CREATE MODEL ステートメントを使用します。
[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["わかりにくい","hardToUnderstand","thumb-down"],["情報またはサンプルコードが不正確","incorrectInformationOrSampleCode","thumb-down"],["必要な情報 / サンプルがない","missingTheInformationSamplesINeed","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-04-02 UTC。"],[[["Contribution analysis, also known as key driver analysis, helps identify changes in key metrics across multi-dimensional data."],["This feature, currently in a pre-GA stage, is available \"as is\" with potential limited support, and subject to the \"Pre-GA Offerings Terms\"."],["Contribution analysis models compare a test data set to a control data set to identify statistically significant changes across various dimensions, such as time or location."],["A `CREATE MODEL` statement in BigQuery can be used to build a contribution analysis model, and these models can use either summable or summable ratio metrics."],["The `ML.GET_INSIGHTS` function allows users to retrieve metric information calculated by a created contribution analysis model."]]],[]]
