In data mining, there are two types of models: Note: There are 2 correct answers to this question.
A. Predictive and explanatory models, which allow one to predict and explain phenomena
B. The SAP Predictive Analytics server must be able to access the name server.
C. The client application must be able to access both the name server and the SAP Predictive Analytics server.
D. Descriptive models, which allow one to describe datasets.
What is time Series?
Note: There are 2 correct answers to this question.
A. Identify and understand the phenomenon represented by your time series.
B. Forecast the evolution of time series in the short and medium term, that is, predict their future values.
C. Different system account, as specified in a "User-Mapping" file. This file will specify, for each authenticated user name the name of a system account to be used. This feature is available only on Linux
D. The system account of the authenticated user, this is the default with the system authentication.
The structure for a continuous variable is defined by several intervals each made of : Note: There are 4 correct answers to this question.
A. a lower bound ([ ]) that can be either open or closed,
B. a minimum value (Minimum)
C. a maximum value (Minimum)
D. With standalone installers on each targeted client system
E. a higher bound ([ ]) that can be either open or closed.
Data modeling with time series is subdivided into four broadly defined stages: Note: There are 4 correct answers to this question.
A. Defining the Modeling Parameters
B. Generating and Validating the Model
C. Analyzing and Understanding the Analytical Results
D. Using a Generated Mode E. The Name Server
A training data file for time series must contain at least two columns: Note: There are 2 correct answers to this question.
A. The date column,
B. The signal column
C. The server port
Once the model has been generated, you must verify its validity by examining the performance indicators: Note: There are 4 correct answers to this question.
A. The predictive power allows you to evaluate the explanatory power of the model, that is, its capacity to explain the target variable when applied to the training dataset. A perfect model would possess a predictive power equal to 1 and a completely random model would possess a predictive power equal to 0
B. The prediction confidence defines the degree of robustness of the model, that is, its capacity to achieve the same explanatory power when applied to a new dataset. In other words, the degree of robustness corresponds to the predictive power of the model applied to an application dataset. For this scenario, the model generated has the following performance indicators:
C. A quality indicator KI equal to 0.808
D. A robustness indicator KR equal to 0.992
E. The existing R is uninstalled and the registry entries and the R installation folder are removed from the machine
Depending on the type of the target, the model graph plot allows you to:
Note: There are 3 correct answers to this question.
A. View the realizable profit that pertains to your business issue using the model generated when the target is nominal.
B. Compare the performance of the model generated with that of a random type model and that of a hypothetical perfect model when the target is nominal.
C. A web server such as Apache Web Server or Windows Internet Information Services (IIS).
D. Compare the predicted value to the actual value when the target is continuous .
The panel Deviation Analysis Debriefing allows you to follow the analysis process thanks to a progression bar. At the end of the process, a debriefing panel is displayed. For details on the debriefing panel, see the topic on Understanding the Deviation Analysis. You can use the toolbar provided on the upper part of the panel to:
Note: There are 3 correct answers to this question.
A. stop the analysis process, by clicking the button,
B. display the text log detailing the process, by clicking the button,
C. copy, print or save the debriefing panel .
D. On a test environment, the software may be installed as a standard application.
A model with a predictive power of:
Note: There are 3 correct answers to this question.
A. "0.79" is capable of explaining 79% of the information contained in the target variable using the explanatory variables contained in the dataset analyzed.
B. "1" is a hypothetical perfect model, capable of explaining 100% of the target variable using the explanatory variables contained in the dataset analyzed. In practice, such a predictive power would generally indicate that an explanatory variable 100% correlated with the target variable was notexcluded from the dataset analyzed.
C. "0" is a purely random model
D. The system account of the authenticated user, this is the default with the system authentication
The Contributions by Variables plot allows you to examine the relative significance of each of the variables within the model. On this plot, each bar represents the contribution of an explanatory variable with respect to the target variable. The following four types of plots allow you to visualize contributions by variables:
Note: There are 4 correct answers to this question.
A. Variable Contributions, that is, relative importance of each variable in the built model.
B. Variable Weights, that is, weights Guides and Scenarios Modeler P U B L I C 97
C. Smart Variable Contributions, that is, the variables internal contributions. Automated Analytics User Guides and Scenarios Modeler P U B L I C 97
D. Maximum Smart Variable Contributions, that is, the maximum smart variable contributions including only the maximum of similar variables. For example, only binned encoding of the continuous variable age will be displayed. This is the chart displayed by default.
E. Perform various analyses and build models on the data, including time series forecasting, outlier detection, trend analysis, classification analysis, segmentation analysis, and affinity analysis.
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