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NEW QUESTION: 1
Azure Machine Learning Studioで線形回帰モデルを開発しています。さまざまなアルゴリズムを比較する実験を実行します。
次の画像は、結果データセットの出力を示しています。
ドロップダウンメニューを使用して、画像に表示される情報に基づいて各質問に回答する回答の選択肢を選択します。
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: Boosted Decision Tree Regression
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Box 2:
Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
NEW QUESTION: 2
You are developing a SQL Server Analysis Services (SSAS) tabular project. A model defines a measure named Profit and includes a table named Date. The table includes year, semester, quarter, month, and date columns. The Date column is of data type Date. The table contains a set of contiguous dates.
You need to create a measure to report on year-over-year growth of profit.
What should you do? (Each answer presents a complete solution. Choose all that apply.)
A. Option C
B. Option B
C. Option A
D. Option D
Answer: A,D
NEW QUESTION: 3
A. Option A
B. Option B
C. Option C
D. Option D
Answer: A
Explanation:
Explanation
First let's see what was configured on interface S0/0/1: