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NEW QUESTION: 1
個々のProvisioning Servicesサーバーに割り当てられたストレージに複数のストアの場所を作成する2つの利点は何ですか？ （2つ選択してください。）
A. vDiskファイルを複製する必要はありません。
B. その分散された性質により、規模を維持するのは簡単です。
C. vDiskのストレージ要件を最小化します。
D. 一元化されたストアの場所よりも少ない構成手順で済みます。
E. それ以上の設定なしで冗長性を提供します。
Answer: B,E
NEW QUESTION: 2
A. Option D
B. Option B
C. Option C
D. Option A
Answer: A
NEW QUESTION: 3
次の各ステートメントについて、ステートメントがtrueの場合は、[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: Yes
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No
Box 3: No
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier.
Reference:
https://www.cloudfactory.com/data-labeling-guide
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
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