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NEW QUESTION: 1
When displaying baseline averages using the automatic time range option, baseline data is correlated by
using the same time period that is being used for the current query for which of the following past number
of intervals?
A. Three
B. Seven
C. Five
D. Ten
Answer: C
Explanation:
Explanation/Reference:
References:
https://kc.mcafee.com/resources/sites/MCAFEE/content/live/PRODUCT_DOCUMENTATION/25000/
PD25226/en_US/esm_940_pg_en-us.pdf Page: 178
NEW QUESTION: 2
You are working as Computer Forensics investigator and are called by the owner of an accounting firm to investigate possible computer abuse by one of the firms employees. You meet with the owner of the firm and discover that the company has never published a policy stating that they reserve the right to inspect their computing assets at will.
What do you do?
A. Inform the owner that conducting an investigation without a policy is a violation of the employees expectation of privacy
B. Inform the owner that conducting an investigation without a policy is a violation of the 4th amendment
C. Inform the owner that conducting an investigation without a policy is not a problem because the company is privately owned
D. Inform the owner that conducting an investigation without a policy is not a problem because a policy is only necessary for government agencies
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
代わりに、順列特徴重要性説明（PFI）を使用してください。
注1：
注2：順列特徴重要性の説明（PFI）：順列特徴重要性は、分類モデルと回帰モデルを説明するために使用される手法です。大まかに言えば、それが機能する方法は、データセット全体に対して一度に1つの機能でデータをランダムにシャッフルし、対象のパフォーマンスメトリックがどの程度変化するかを計算することです。変更が大きいほど、その機能はより重要になります。 PFIは、基礎となるモデルの全体的な動作を説明できますが、個々の予測を説明することはできません。
B. はい
Answer: A
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability