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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Explanation
The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.
Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.
Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.
Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words:
ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
AUC.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
NEW QUESTION: 2
An alarm system whereby the central station is owned by and located within the installation being
protected is known as a(n):
A. Bell-sound system
B. Proprietary system
C. Auxiliary system
D. Central station system
E. Local alarm system
Answer: B
NEW QUESTION: 3
この質問では、太字のテキストを評価して正しいかどうかを判断する必要があります。
企業は、分類とラベルを自動的に適用して、ドキュメントとメールを保護する必要があります。コストを最小限に抑える必要があります。
Information Rights Managementを実装する必要があります
下線付きのテキストを確認します。ステートメントが正しい場合は、[変更は必要ありません]を選択します。ステートメントが間違っている場合は、ステートメントが正しい答えの選択肢を選択してください。
A. Azure Information Protection Premium P2
B. Azure Information Protection Premium P1
C. 変更する必要はありません。
D. Microsoft 365 E3
Answer: A
Explanation:
Explanation
References:
https://azure.microsoft.com/en-us/pricing/details/information-protection/
NEW QUESTION: 4
DRAG DROP
Answer:
Explanation: