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NEW QUESTION: 1
管理者が、デモ用の "Request"を含むProdot Lauth 2.0のランディングページを作成します。 管理者は、自動化ルールによって送信される「製品フォローアップ」電子メールテンプレートも作成しました。 ルールは次のように設定されています。
次のとおりです。
マッチタイプ：見込みフォーム '' Demo Request ''は正常に完了しました。
アクション：見込みの電子メール「製品の関心のフォローアップ」を送信してください。
このフォームを送信した見込み客には、フォローアップの電子メールがどのくらいの頻度で送信されますか。
A. 「Product Launch 2.0」ランディングページからフォームを送信する見込み客が既知の見込み客である場合にのみ、電子メールが送信されます。
B. フォームが「Product Launch 2.0」のランディングページから何回送信されても、電子メールは送信されません。
C. 「Product Launch 2.0」のランディングページからフォームが送信されるたびにEメールが送信されます。
D. 電子メールは、見込み客が「Product Launch 2.0」のランディングページからフォームを最初に送信したときにのみ送信されます。
Answer: A
NEW QUESTION: 2
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:
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: 3
HOT SPOT
Answer:
Explanation:
Explanation
Target1: [ValidateAntiForgeryToken]
Target2: @Html.AntoForgeryToken()
Example:
* At the top of the action that we created to handle the posted form, the one with the [HttpPost] attribute added, we'll add another attribute named [ValidateAntiForgeryToken]. This makes the start of our action now look like the following:
[HttpPost]
[ValidateAntiForgeryToken]
public ActionResult ChangeEmail(ChangeEmailModel model)
{
string username = WebSecurity.CurrentUserName;
*rest of function omitted*
* we must add the unique token to the form to change the user's email when we display it. Update the form in the ChangeEmail.aspx view under /Account/ChangeForm:
<% using(Html.BeginForm()) { %>
<%: Html.AntiForgeryToken() %>
<%: Html.TextBoxFor(t=>t.NewEmail) %>
<input type="submit" value="Change Email" />
<% } %>
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