SAP C_PAII10_35 dumps - 100% Pass Guarantee!

Once the models have been generated, model performance indicators, plots and modeling reports in HTML format facilitate viewing and interpretation of the data modeling results. Once the models have been validated, you can apply them to :

Note: There are 2 correct answers to this question.

A. One or more specific observations taken from your database

B. A new, complete dataset or application dataset

C. System authentication is to be used through Pluggable Authentication Module (PAM). Access to Linux System account password required root privileges.

You can then generate models, capable of either explaining and predicting a phenomenon, or describing a dataset, in both cases, as a function of the previously defined target variable. This phase is called the training phase. Once the models have been generated, you can view and interpret their relevance and robustness using: Note: There are 2 correct answers to this question.

A. Performance indicators: the predictive power, which is the quality indicator, and the prediction confidence, which is the robustness indicator.

B. A variety of plots, including the profit curve plot.

C. knows how to set up ODBC drivers on Windows systems

D. has administration rights on the machine,

For data and performance management purposes, the dataset to be analyzed must contain a variable that serves as a key variable. Two cases should be considered:

Note: There are 2 correct answers to this question.

A. If the initial dataset does not contain a key variable, a variable index Kx Index is automatically generated by the application. This will correspond to the row number of the processed data.

B. If the file contains one or more key variables, they are not recognized automatically. You must specify them manually in the data description.

C. the user who runs SAP Predictive Analytics is also the one used to install the software

The term model carries many different meanings depending on its field of application. In data mining, a model describes and explains the relationships that exist between input data (one or more target variablest allows one to predict and explain phenomena, or to describe them. To quote George E.P. Box, "All Models Are Wrong But Some Are Useful" from "Robustness is the Strategy of Scientific Model Building" in Robustness in Statistics. Eds., R.L. Launer and G.N. Wilkinson, 1979, Academic Press. A model that has satisfactory performance is one that possesses both:

Note: There are 2 correct answers to this question.

A. High explanatory power, that is, sufficient capacity to explain the target variable. This explanatory power is indicated by the predictive power of the model.

B. High robustness, that is, sufficient capacity to repeat the same performance on new datasets containing observations of a similar nature to the training dataset. This explanatory power is indicated by the prediction confidence .

C. Both of these ports must be accessible from the client machine. For example, network firewalls should allow communication on these ports between client machine and the servers.

Understanding the Decision Tree Panel The panel Decision Tree is split into three parts: Note: There are 3 correct answers to this question.

A. The decision tree itself, which is displayed in the upper section of the panel

B. Two tabs located in the left bottom part of the panel provide you with information on the nodes and with the profit curve corresponding to the current decision tree

C. A navigator allowing you to visualize what part of the tree you are studying is displayed in the right bottom part of the panel.\

D. A consequence of 1 is that this metric is resistant to outliers: when there are a few occurrences of the target with very high values with respect to the rest of the target value distributions, the notion of normal profit is not impacted.