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Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/ja/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"gridClassNames":"aem-Grid aem-Grid--12 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href=\"https://www.snowflake.com/en/fundamentals/predictive-modeling/\"\u003E予測\u003C/a\u003Eやレコメンデーションを行います。決定木は、問題をシンプルなステップに分割して結果を理解しやすくするため、MLで広く使われている手法です。\u003C/p\u003E\n\u003Cp\u003E決定木は、教師あり学習でよく使用されます。教師あり学習では、モデルはすでに既知の正解を持っている例から学習します。一般的に、スパムEメールの特定などの分類タスクや、建物のエネルギー使用量の予測などの回帰タスクを処理します。決定木の推論プロセスを可視化し、解釈できる点こそが、他と一線を画す特徴です。データドリブンなさまざまな問いに応じて「分岐」が異なる方向に分かれていく様子を観察することで、モデルの推論がどのようにして結果につながったのかが明らかになります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn 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snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn 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snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"and","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-2ae39823a9","additionalClasses":"headline-decoration","type":"heading2","lines":["決定木の種類"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ed4355f982","text":"\u003Cp\u003E多数の一般的な決定木アルゴリズムが利用可能であり、そのほとんどは分類タスクや回帰タスクに適用できます。代表的なものには、次のアルゴリズムがあります。 \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ECART（分類木と回帰木）\u003C/h3\u003E\r\n\u003Cp\u003ECARTは、広く使用されているアルゴリズムです。他の決定木の手法とは一線を画しています。CARTは、データ内の値を最も適切に分離する分割に着目して、特徴量ごとに常にバイナリ分割（はい/いいえ）を作成します。たとえば、ローンを承認するかどうかを予測するCARTモデルは、まず申請者を「収入 > 5万ドル」（はい/いいえ）で分割し、続いて申請者のクレジットスコアが750以上かどうか、申請者が雇用されているかどうかなどの他の要因に基づいて各グループを分割します。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EID3（Iterative Dichotomiser 3）\u003C/h3\u003E\r\n\u003Cp\u003EID3は、人気の高い決定木アルゴリズムの一つであり、可能な回答を徐々に絞り込み、最終的な予測やレコメンデーションに到達するまで、データを小さなグループに分割します。たとえば、スパムフィルターでは、商用広告でよく使用される「オファー」という単語を含むEメールを特定できます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EC4.5\u003C/h3\u003E\r\n\u003Cp\u003EC4.5は、データを小さなグループに分ける一連のはい/いいえの質問をすることで決定木を構築するため、より正確な予測に到達しやすくなります。カテゴリー値（「スパム」や「スパムでない」など）と数値（「年齢」や「所得」など）の両方を処理しながら、欠落データのようなギャップに対処できるため、ID3を改良したアルゴリズムです。通信業界を例に挙げると、C4.5アルゴリズムを活用することで、年齢、居住地、データ使用量といった要素を多角的に分析し、たとえ見込み顧客の情報が一部欠けていたとしても、顧客一人ひとりに最適なプラン案を提示できます。 \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ECHAID（カイ二乗自動相互作用検知）\u003C/h3\u003E\r\n\u003Cp\u003ECHAIDは、統計テストを使用して分割先を決定し、多くの場合、複数のオプションを含む分岐を一度に作成します。たとえば、小売企業は顧客を10代、若年成人、中高年、シニアなどの年齢層に分類して、どのデモグラフィックグループが新しいロイヤルティプログラムに反応する可能性が最も高いかを予測することができます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E条件付き推論ツリー\u003C/h3\u003E\r\n\u003Cp\u003E条件付き推論ツリーは、変数が分岐を正当化するのに十分な関連性を持つかどうかをテストすることでバイアスを低減します。この点では、ID3やCARTのような、ある要因が統計的に有意かどうかをテストせずにデータを段階的に分割する通常の決定木とは異なります。たとえば、通常の決定木では「大学に通ったかどうか」が優先されますが、条件付き推論ツリーではジョブのパフォーマンス予測に統計的に無関係であると見なされる場合があります。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_259718203":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-e38322a2fe","additionalClasses":"headline-decoration","type":"heading2","lines":["決定木の分割条件"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-6ceb287bbc","text":"\u003Cp\u003Eデータを分割する場合、MLモデルは通常、次の2つの一般的な決定木の基準のいずれかを使用します。それはジニ不純度またはエントロピーです。それぞれがデータの構成を測定し、アルゴリズムが選択した方法を適用して、データを最も効果的に分割する分割を見つけます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003Eジニ不純度\u003C/h3\u003E\r\n\u003Cp\u003Eジニは、質問によってデータがどの程度明確に区分されているかを調べます。数学的には、ランダムな項目がグループの分布に従ってラベル付けされた場合に誤って分類される可能性を反映しています。CARTアルゴリズムは、この測定値を適用してさまざまな分割をテストし、最も明確な分割を選択します。たとえば、疲れているかどうかを尋ねることで、コーヒーを飲みそうな人と飲まない人の2つのグループに分かれる場合などです。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003Eエントロピー\u003C/h3\u003E\r\n\u003Cp\u003Eエントロピーはデータセットの不確実性を測定します。ID3やC4.5などのアルゴリズムは、情報利得の計算にエントロピーを利用します。これは、分割の結果生じる不確実性の減少を表します。決定木は不確実性を最も軽減する分割を選択するため、クラス間の分離が最も明確になります。コーヒーの事例では、午前か午後かを尋ねることで、意思決定の指針となるグループが明確になるため、不確実性が軽減されます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_289473020":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"equation","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-2250b98ae1","additionalClasses":"headline-decoration","type":"heading2","lines":["決定木の使用目的"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-b10718f417","text":"\u003Cp\u003E機械学習では、決定木はモデルが生データを有用なインサイトに変換できるようにします。これは、意思決定に十分な裏付けと信頼性が求められる業界で特に有用です。\u003C/p\u003E\n\u003Cp\u003E以下に、決定木の一般的な使用例を示します。 \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E事業戦略と計画\u003C/h3\u003E\n\u003Cp\u003E決定木でトレーニングされたMLモデルは、売上成長、価格トレンド、顧客チャーン、サプライチェーンの需要や在庫レベルなどの予測に役立ちます。 \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003Eリスク評価と緩和\u003C/h3\u003E\n\u003Cp\u003E金融・保険業界において、決定木はデフォルト、保険金請求、その他の損失などのリスク評価に役立ちます。信用履歴、所得レベル、請求パターンなどの顧客データの分岐経路をたどることで、保険数理人、引受人、金融アナリストがより正確なリスク推定を提供できるようになります。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E顧客のセグメンテーションとターゲティング \u003C/h3\u003E\n\u003Cp\u003Eマーケターは、決定木モデルを使用して、購買行動、デモグラフィック、オンラインアクティビティに基づいて\u003Ca href=\"https://www.snowflake.com/ja/fundamentals/customer-segmentation/\"\u003E顧客\u003C/a\u003Eをグループ分けすることができます。これにより、企業はよりパーソナライズされたオファーを提供し、どの顧客がキャンペーンに反応する可能性が最も高いかを予測することができます。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E医療診断と治療\u003C/h3\u003E\n\u003Cp\u003EヘルスケアのMLモデルは、患者データの解釈に決定木に頼ることがよくあります。たとえば、あるモデルで症状を測定、検査結果を検討し、家族の病歴を調べることで、診断や治療の指針となる重要な情報を収集することができます。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E金融不正検知\u003C/h3\u003E\n\u003Cp\u003E銀行など、リスクを回避する金融機関は、決定木モデルを使用して疑わしいアクティビティを検出できます。モデルは、購入金額や取引規模、返品などのパターンを分析することで、潜在的な詐欺、マネーロンダリング、その他の潜在的な犯罪活動を示す取引を特定できます。 \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_copy_copy__1985496925":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"why","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-bff30a5fe3","additionalClasses":"headline-decoration","type":"heading2","lines":["決定木のメリット"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-c96fcc769f","text":"\u003Cp\u003E決定木は、時間のかかる推論プロセスを簡素化し、より迅速かつ効率的に結果を提供します。具体的なメリットは以下のとおりです。 \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E容易な解釈可能性\u003C/h3\u003E\r\n\u003Cp\u003E決定木の透明性は、MLの推論プロセスから不明な点を取り除くのに役立ちます。モデルが結論やレコメンデーションに至るまでのステップバイステップのロジックを、誰でも視覚的に追えるようになります。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003Eデータ準備負荷の軽減\u003C/h3\u003E\r\n\u003Cp\u003E決定木はカテゴリと数値の両方を扱えるため、アナリストはデータの変換や再フォーマットに多くの時間を費やす必要はありません。モデルを実行する事前の準備作業を軽減できます。 \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E高い柔軟性\u003C/h3\u003E\r\n\u003Cp\u003E決定木は、それぞれが独立して予測を行える自己完結型モデルであるため、さまざまな問題に適応できます。また、柔軟な設計により、多くの決定木を組み合わせて出力を集約できるため、大規模で複雑なタスクを処理できます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E欠落している値への対応\u003C/h3\u003E\r\n\u003Cp\u003E完全なデータセットを必要とする一部のモデルとは異なり、決定木は情報が不足している場合にも機能できます。そのためには、不完全なレコードに低い重みを割り当てたり、複数の可能なパスにデータを分割したりすることで対応します。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E小規模なデータセットでの使用に最適\u003C/h3\u003E\r\n\u003Cp\u003E決定木は、膨大なデータなしに有用なパターンを見つけられます。情報が限られている場合でも有効であるため、データの不足や収集に時間のかかる分野では役立ちます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn 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snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn 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