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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. No
B. Yes
Answer: B
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
You customize the Define Search String for Electronic Bank Statement node. What do you enter into the target field?
A. The posting rule
B. The value that is derived
C. The value to be searched
D. The clearing algorithm
Answer: B

NEW QUESTION: 3
Used in conjunction, which of the following are PII? (Select TWO).
A. Birthday
B. Full name
C. Favorite movie
D. Pet's name
E. Marital status
Answer: A,B
Explanation:
Personally identifiable information (PII) is a catchall for any data that can be used to uniquely identify an individual.
This data can be anything from the person's name to a fingerprint (think biometrics), credit card number, or patient record. A birthday together with a full name makes it personally identifiable information.
Incorrect Answers:
A. Marital status can be shared and thus is not personally identifiable information.
B. Many people can share a like for the same movie.
C. Pet's name is not personally identifiable information.
References:
Dulaney, Emmett and Chuck Eastton, CompTIA Security+ Study Guide, 6th Edition, Sybex, Indianapolis, 2014, p. 404

NEW QUESTION: 4
AppScan supports full call and data flow analysis for mobile applications with which languages?
A. Visual Basic, COBOL
B. HTML, .Net
C. C++, Perl, Cold Fusion
D. Java, JavaScript, Object-C
Answer: D