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NEW QUESTION: 1
分類タスクを解決しています。
データセットが不均衡です。
あなたは、分類精度を向上させるためにAzureの機械学習Studioのモジュールを選択する必要があります。
あなたはどちらのモジュールを使用する必要がありますか?
A. フィッシャー線形判別分析。
B. フィルタに基づく機能の選択
C. 順列機能の重要性
D. の合成少数オーバーサンプリング技術(撃ち)
Answer: D
Explanation:
Explanation
Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
NEW QUESTION: 2
A. Option A
B. Option B
C. Option C
D. Option D
Answer: B
NEW QUESTION: 3
What is the primary benefit of implementing the Intelligent Monitoring Framework feature for resources?
A. immediate notification of resource state
B. monitoring of cluster-to-cluster heartbeat
C. monitoring of Virtual machines
D. monitoring application outside cluster configuration
Answer: A
Explanation:
Explanation/Reference:
Reference: https://sort.veritas.com/public/documents/vie/7.1/windows/productguides/html/vcs_admin/ ch08s03.htm