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NEW QUESTION: 1
Given:

そして、コードの断片を考えると：

結果は何ですか？
A. Compilation fails at line n2.
B. Compilation fails at line n1.
C. Compilation fails at line n3.
D. Read E-Book
E. Read Book
Answer: C

NEW QUESTION: 2
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

NEW QUESTION: 3
Your client has sales tax requirements that involve two types of sales tax. The first sales tax applies to all customers. The second sales tax is based on where the company ships the goods. All goods are taxable; however some customers who resell the goods are exempted from the second sales tax. Choose three applicable options to set up sales tax for your client. (Choose three.)
A. Item exemptions for both sales taxes
B. Tax classification code for exempt tax
C. Customer tax exemptions for the second sales tax
D. Customer exemptions for the first sales tax
E. Tax group for the combination of the two tax classification codes
Answer: B,C,E

NEW QUESTION: 4
Which one of these devices can be used to output data from a computer?

Answer:
Explanation: