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NEW QUESTION: 1
トレーニングと検証のエラー値に大きな差があるモデルがあります。
新しいモデルを作成し、相互検証を実行する必要があります。
Azure Machine Learning Studioを使用して、新しいモデルのパラメーターセットを識別する必要があります。
各ステップで使用するモジュールはどれですか？答えるには、適切なモジュールを正しい手順にドラッグします。
各モジュールは、1回または複数回使用することも、まったく使用しないこともできます。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には1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

NEW QUESTION: 2
Mit welchem ​​Dienstprogramm kann geändert werden, wie oft eine Dateisystemprüfung für ein ext2-Dateisystem durchgeführt wird, ohne dass auf diesem Dateisystem gespeicherte Daten verloren gehen?
A. mod2fs
B. fixe2fs
C. mke2fs
D. fsck
E. tune2fs
Answer: E

NEW QUESTION: 3

Sales.Customers

Application.Cities

Sales.CustomerCategories

Your company is developing a new social application that connects customers to each other based on the distance between their delivery locations.



A. Option B
B. Option A
Answer: A

NEW QUESTION: 4
You have been tasked with creating a wireless link between two buildings on a single campus. The link must support at least 150 Mbps data rates. What kind of WLAN technology role should you deploy?
A. WPAN
B. Access BSS
C. Wireless bridging
D. IBSS
Answer: D
Explanation:
Explanation/Reference:
Reference: https://www.theseus.fi/bitstream/handle/10024/130087/Mengdi_Ji.pdf?sequence=1