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NEW QUESTION: 1
Azure Machine Learning Studioで実験を作成します。 10,000行を含むトレーニングデータセットを追加します。最初の9,000行はクラス0（90パーセント）を表します。
残りの1,000行はクラス1（10パーセント）を表します。
トレーニングセットは、2つのクラス間の不均衡です。 5つのデータ行を使用して、クラス1のトレーニング例の数を4,000に増やす必要があります。 Synthetic Minority Oversampling Technique（SMOTE）モジュールを実験に追加します。
モジュールを構成する必要があります。
どの値を使用する必要がありますか？回答するには、回答領域のダイアログボックスで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION: 2
다음 중 조직의 비즈니스 연속성 계획에 적합한 테스트를 나타내는 것은 무엇입니까?
A. 기능 영역과 관련된 종이 테스트
B. 응급 사이트에서의 컴퓨터 작동에 대한 전체 테스트
C. 기술 공급 업체와의 계획 연습
D. 유사한 조직에 대한 계획을 벤치마킹
Answer: A

NEW QUESTION: 3
After performing the following operations:

What will be the value of $a?
A. None of the above
B. array('c', 'b', 'a')
C. array(2, 1, 0)
D. array('a', 'b', 'c')
Answer: D