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NEW QUESTION: 1
DRAG DROP
クラウド環境の特性を左側から右側の正しい例にドラッグアンドドロップします。
Answer:
Explanation:
NEW QUESTION: 2
A client has implemented a revenue policy with time-based contingencies. An invoice for a creditworthy customer is imported and one of the invoice lines is associated with both a nonstandard refund policy (60 days) and an acceptance clause (100 days).
When will revenue be recognized?
A. after 100 days
B. immediately
C. after 60 days
D. only after manual intervention
Answer: A
NEW QUESTION: 3
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、述べられた目標を達成する可能性のある独自の解決策が含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
ロジスティック回帰アルゴリズムを使用して分類モデルをトレーニングします。
各機能の重要度を、全体的なグローバル相対重要度値として、および特定の予測セットのローカル重要度の尺度として計算することにより、モデルの予測を説明できる必要があります。
必要なグローバルおよびローカルの特徴重要度値を取得するために使用できる説明を作成する必要があります。
解決策：TabularExplainerを作成します。
ソリューションは目標を達成していますか？
A. いいえ
B. はい
Answer: A
Explanation:
Explanation
Instead use Permutation Feature Importance Explainer (PFI).
Note 1:
Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability