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NEW QUESTION: 1
The nurse is caring for a 3-month-old girl with meningitis. She has a positive Kernig's sign. The nurse expects her to react to discomfort if she:
A. Turns her head to the side
B. Plantiflexes her wrist
C. Flexes her spine
D. Dorsiflexes her ankle
Answer: C
Explanation:
Explanation
(A) Discomfort with ankle dorsiflexion is not expected with meningitis. (B) Spinal flexion, flexing the neck or the hips with legs extended, causes discomfort if the meninges are irritated. (C) Discomfort with wrist flexion is not expected with meningitis. (D) Rotating the cervical spine may cause discomfort with meningitis, but pain with flexion is more indicative of meningeal irritation.

NEW QUESTION: 2
Which task must be done before a zone set takes effect?
A. Add a member to the zone.
B. Enter the exit config t command.
C. Enter the copy running-config startup-config command.
D. Enter the zoneset activate name <zone-name> vsan <vsan-#> command.
Answer: D
Explanation:
Explanation/Reference:
Explanation:

NEW QUESTION: 3
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、記載された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
このセクションの質問に回答すると、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
次の変数に応じて、学生のアートワークの価格を予測するモデルを作成しています：学生の教育期間、学位タイプ、および芸術形態。
まず、線形回帰モデルを作成します。
線形回帰モデルを評価する必要があります。
解決策：次のメトリックを使用します：平均絶対誤差、ルート平均絶対誤差、相対絶対誤差、相対二乗誤差、および決定係数。
ソリューションは目標を達成していますか？
A. はい
B. いいえ
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.
参照：
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model