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NEW QUESTION: 1
프로젝트 실행 단계에서 새 기능 관리자가 회사에 합류하여 이전 기능 관리자를 대체합니다. 월간 미팅 중에 새 기능 관리자는 회의에서 제공된 데이터에 대해 혼동을 표시하고 이 프로젝트가 프로젝트에 영향을 주기 때문에 이메일을 통해 매주 보고서를 요청합니다 부서의 업무가 크게 확인 됨 프로젝트 관리자는 이전 기능 관리자가 매주 이메일 보고서를 받지 못했다는 것을 알게 되었습니다.
이 문제의 원인은 무엇입니까?
A. 새로운 기능 관리자가 통신 관리 계획을 검토하지 않았습니다.
B. 전 기능 관리자가 보고서 수신을 요청하지 않았습니다.
C. 통신 관리 계획이 제대로 수정되지 않았습니다
D. 이해 관계자 참여 계획이 제대로 작성되지 않았습니다.
Answer: C
NEW QUESTION: 2
Azure Machine Learning Studioで多重線形回帰モデルを作成しています。
いくつかの独立変数は高度に相関しています。
すべてのデータに対して効果的な特徴エンジニアリングを行うための適切な方法を選択する必要があります。
どの3つのアクションを順番に実行する必要がありますか？回答するには、適切なアクションをアクションのリストから回答領域に移動し、正しい順序に並べます。
Answer:
Explanation:
Explanation:
Step 1: Use the Filter Based Feature Selection module
Filter Based Feature Selection identifies the features in a dataset with the greatest predictive power.
The module outputs a dataset that contains the best feature columns, as ranked by predictive power. It also outputs the names of the features and their scores from the selected metric.
Step 2: Build a counting transform
A counting transform creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets.
Step 3: Test the hypothesis using t-Test
References:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/studio-module-reference/filter-based-feature-selection
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/build-counting-transform
NEW QUESTION: 3
Eine gültige multiple lineare Regression (MLR) ist durch alle diese gekennzeichnet, außer?
A. Es wird angenommen, dass die X (Eingänge) unabhängig voneinander sind
B. Es ist nicht möglich, Interaktionen in einer MLR-Analyse zu bewerten
C. Die Residuen aus der MLR-Analyse müssen normalverteilt sein
D. Es wird davon ausgegangen, dass die X (Eingänge) nicht miteinander korreliert sind
E. MLR wird basierend auf einer absichtlichen Form des Experimentierens durchgeführt
Answer: E