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NEW QUESTION: 1
さまざまな都市の家の所有権に関する人口統計データを探索する予定です。データは、次の形式のCSVファイルです。
年齢、都市、収入、自宅所有者
21,Chicago,50000,0
35,Seattle,120000,1
23,Seattle,65000,0
45,Seattle,130000,1
18,Chicago,48000,0
データを探索して結果を記録するには、Azure Machine Learningワークスペースで実験を実行する必要があります。実験では、次の情報を記録する必要があります。
*データセット内の観測数
* home_ownerによる収入の箱ひげ図
*都市名と各都市の平均収入を含む辞書必要な情報をログに記録するには、実験の実行オブジェクトの適切なロギングメソッドを使用する必要があります。
どのようにコードを完成させる必要がありますか？回答するには、適切なコードセグメントを正しい場所にドラッグします。各コードセグメントは、1回、複数回、またはまったく使用しない場合があります。コンテンツを表示するには、ペイン間で分割バーをドラッグするか、スクロールする必要がある場合があります。
注：それぞれの正しい選択は1ポイントの価値があります。
Answer:
Explanation:
Explanation:
Box 1: log
The number of observations in the dataset.
run.log(name, value, description='')
Scalar values: Log a numerical or string value to the run with the given name. Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.
Example: run.log("accuracy", 0.95)
Box 2: log_image
A box plot of income by home_owner.
log_image Log an image to the run record. Use log_image to log a .PNG image file or a matplotlib plot to the run. These images will be visible and comparable in the run record.
Example: run.log_image("ROC", plot=plt)
Box 3: log_table
A dictionary containing the city names and the average income for each city.
log_table: Log a dictionary object to the run with the given name.
NEW QUESTION: 2
The sticky bit is usually set on
A. log liles
B. /tmp
C. /var/log
D. /home
Answer: B
NEW QUESTION: 3
What 4 information contains the sap logon ticket?
A. User Id,
B. Issuing system's digital signature
C. Validity period,
D. Issuing System ID,
E. User passcode
Answer: A,B,C,D
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