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 existing R is uninstalled and the registry entries and the R installation folder are removed from the machine.
• B. A quality indicator KI equal to 0.808,
• C. 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.
• D. A robustness indicator KR equal to 0.992.
• E. 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:

To Define the Risk Mode Parameters:
Note: There are 4 correct answers to this question.

• A. Indicate the increase of score points needed to double the odds in the field Points to double odds.
• B. In the field for good/bad odds ratio of, enter the ratio.
• C. The existing R is uninstalled and the registry entries and the R installation folder are removed from the machine
• D. Click the button View Score Table to display the table of scores associated with the corresponding good/bad odds ratio.
• E. In the field Risk Score, enter the score you want to associate with a good/bad odds ratio.

The Periodic with testing at the end strategy distributes:
Note: There are 4 correct answers to this question.

• A. Three lines of the first 4/5 of the initial dataset are distributed to the training subset.
• B. One line of the first 4/5 of the initial dataset is distributed to the validation sub-set.
• C. The name server and the server will be assigned a specific TCP port. By default, the startup scripts we use fix them, and they should probably set properly depending on the network strategy
• D. If the entire 4/5 of the initial dataset is not yet distributed, distribution operations begin again at step 1. Otherwise, if the entire 4/5 of the initial dataset has been distributed, distribution operations go to step
• E. The final 1/5 of the initial dataset is sent as a block of data to the testing sub-set.

In the large majority of cases, a first degree polynomial is sufficient for generation of a relevant and robust model. Using a higher degree of polynomial does not always guarantee better results than those obtained with a first degree polynomial. In addition, the higher the degree of polynomial you select:
Note: There are 3 correct answers to this question.

• A. The harder it is to interpret the results of modeling.
• B. The more time needed to generate the corresponding model,
• C. The server pass phrase (optional)
• D. The more time needed to apply the model to new datasets,

The Decision Tree Each node in the tree displays:
Note: There are 4 correct answers to this question.

• A. The Population of the node
• B. The ratio of Positive Target (for nominal targets) or the Target Mean (for continuous targets)
• C. The categories on which the node population has been filtered, for example
  {Married-AFspouse;Never- married}
• D. The name of the expanded variable, for example Marital-status.
• E. The Usage entries are sparser and report a more global view of the activity on the server. They consist of one line per event on the following events: user connection, user disconnection, training a model, applying a model, saving a model.