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NEW QUESTION: 1
機械学習モデルを作成しています。 null行を含むデータセットがあります。
Azure Machine Learning StudioのClean Missing Dataモジュールを使用して、データセット内のnullデータと欠落データを特定して解決する必要があります。
どのパラメーターを使用する必要がありますか?
A. ホットデッキ
B. 平均で置換
C. 列全体を削除します
D. 行全体を削除します
Answer: C
Explanation:
Explanation
Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if the missing value can be considered randomly missing.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
NEW QUESTION: 2
Corporate social responsibility (CSR) encompasses four different dimensions.
Match each of the descriptions below with the correct CSR dimension.
Answer:
Explanation:
NEW QUESTION: 3
Cisco 642-996 Exam
Which three items represent features of VM-FEX with Cisco Nexus 1000V? (Choose three)
A. High performance
B. Software packet switching
C. Feature rich
D. 802.1 Q
E. VN-Tag (802.1 Qbh)
F. Hardware packet switching
Answer: A,C,F
NEW QUESTION: 4
An analyst knows that the categorical predictor, storeId, is an important predictor of the target.
However, store_Id has too many levels to be a feasible predictor in the model. The analyst wants to combine stores and treat them as members of the same class level.
What are the two most effective ways to address the problem? (Choose two.)
A. Cluster by using Greenacre's method to combine stores that are similar.
B. Eliminate store_id as a predictor in the model because it has too many levels to be feasible.
C. Use subject matter expertise to combine stores that are similar.
D. Randomly combine the stores into five groups to keep the stochastic variation among the observations intact.
Answer: A,C