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NEW QUESTION: 1
Look at the following screenshot and select the BEST answer.
A. Clients external to the Security Gateway can upload any files to the FTP_Ext-server using FTP.
B. Clients external to the Security Gateway can download archive files from FTP_Ext server using FTP.
C. Internal clients can upload and download archive-files to FTP_Ext server using FTP.
D. Internal clients can upload and download any-files to FTP_Ext-server using FTP.
Answer: B
NEW QUESTION: 2
You are a project manager who uses Project Standard 2013, and your task list includes a flag field that indicates, with Yes or No, if a non-summary task represents a physical deliverable.
Your project involves several tasks marked as deliverables, and management requires you to track how many deliverables are completed. The deliverable is considered completed when the Percent complete field is 100%.
You need to create a formula to calculate the total number of deliverables that are completed.
Which two steps should you perform? (Each correct answer presents part of the solution.
Choose two.)
A. Use the following formula: IIf([Physical Deliverable]=Yes And [% Complete]=100,1,0).
B. Set Calculation for tasks and summary groups to Count All.
C. Set Calculation for tasks and summary groups to Sum.
D. Use the following formula: IIf([Physical Deliverable]=Yes And [% Complete]=100,0,1).
Answer: A,C
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. いいえ
B. はい
Answer: B
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
