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NEW QUESTION: 1

A. Option A
B. Option D
C. Option B
D. Option C
Answer: A

NEW QUESTION: 2
特徴抽出方法を選択する必要があります。
どの方法を使用する必要がありますか？
A. ケンドール相関
B. 順列機能の重要性
C. 相互情報
D. ムードの中央値検定
Answer: A
Explanation:
Explanation
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter ), is a statistic used to measure the ordinal association between two measured quantities.
It is a supported method of the Azure Machine Learning Feature selection.
Scenario: When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules

NEW QUESTION: 3
Welcher der folgenden Programmtypen kann Bildschirme enthalten?
Es gibt 3 richtige Antworten auf diese Frage
A. Ausführbare Programme
B. Modulpools
C. Schnittstellenpools
D. Funktionsgruppen
E. Klassenpools
Answer: A,B,D

NEW QUESTION: 4
Each OSPF router advertises LSAs to the outside through the flooding link state. Which ones are included?
(multiple choice)
A. Available ports
B. reachable neighbors
C. Local link status information
D. Information about neighboring routers
Answer: A,B,C