To Select a Profit Type ;
Note: There are 2 correct answers to this question.
A. Select a profit type. The corresponding profit curves appear.
B. The server port (by default: 12347 for the authenticated server).
C. On the screen Model Curves, beneath the plot, click the dropdown list associated with the Profit field. The list of profit types appears.
If you let the structure as undefined, the application uses consistent coder to automatically determine the categories grouping depending on their interaction with the target variable. You can configure two parameters in this case:
Note: There are 2 correct answers to this question.
A. listing active connections, with information such as user, last activity time stamp, number of current models,
B. listing the state of models being within each connection,
C. The band count for continuous variables
D. Modeler - Data Encoding optimal grouping for all variables
How are missing values handled in Automated Analytics? Note: There are 1 correct answers to this question.
A. They are automatically ignored.
B. They are assigned to a category.
C. They are automatically filled.
D. They are considered as outliers.
A variable corresponds to an attribute which describes the observations stored in your database. For example, in a database containing information about your customers, the "name" and "address" of those customers are examples of variables. In Automated Analytics, a variable is defined by three aspects:
Note: There are 2 correct answers to this question.
A. The type of variable: Continuous Variables whose values are numerical, continuous, and sortable. They can be used to calculate measures are sortable. Nominal Variables
B. with discrete values that are not sortable. Textual A type of nominal variable containing phrases, sentences or complete texts. Textual variables are used for text analyses.
C. The storage format of the variable
D. The role of the variable: Target, Explanatory, or Weight
E. With standalone installers on each targeted client system.
The section Control for Deviation Overview provides you with basic statistics on the Dataset used for Deviation Control
Note: There are 4 correct answers to this question.
A. the name of the dataset (Dataset)
B. the source file (Source)
C. A web server (Apache Web Server for example). See the sections below to install Apache Web Server
D. the number of records contained in the dataset (Number of Records)
E. and the number of variables for which the application has found deviations in comparison to the dataset originally used to train the model (Number of variables showing deviation)
For formats are supported for the Date column: Note: There are 4 correct answers to this question.
A. datetime (ISO format: yyyy-mm-dd hh:mm )which has an hour-precision.
B. date (ISO format: yyyy-mm-dd) which has a day-precision.
C. Number(number, for example "seconds")
D. Java Runtime Environment installed on the client machine to connect to the server.
The option Analyze Deviations is a tool that provides you with a diagnostic of the data statistical variation. This option can be used for several purposes:
Note: There are 3 correct answers to this question.
A. to compare the distribution of a new dataset with the distribution of the dataset used to train the model,
B. to check the quality of new data after loading them,
C. to check if your data have evolved over time and thus if the model need to be adapted to the new data.
D. The Root entries are used for tracing the activity of the server which can later be used for debugging purposes.
The analytics are generated during a phase called the training phase, using a training dataset. Depending on the situation, the training dataset may be cut into three sub-sets:
Note: There are 3 correct answers to this question.
A. A Training sub-set.
B. A Validation sub-set.
C. The proper TCP port,
D. A Testing sub-set
Using Regression/Classification, you have contacted the prospects most likely to be interested in your new financial product, and identified the ideal number of prospects to contact out of the entire database meeting the deadlines and within the budget you were allowed. To improve the rate of return of your campaign, senior management might ask you to:
Note: There are 2 correct answers to this question.
A. Build a segmentation model of your customers,
B. Analyze the characteristics of the identified clusters,
C. The Name Server (usually port 12345).
D. Define customized communications for each cluster.
The different types of variables: continuous, ordinal and nominal are next encoded by the data encoding feature of Modeler, or by the Event Logging and Sequence Coding features in the case of dynamic data. Before generating the model, you must:
Note: There are 2 correct answers to this question.
A. Describe the data. A utility integrated with the application allows you to generate a description of the dataset to be analyzed, automatically. You need only validate that description, verifying that the type and storage format of each variable were identified correctly.
B. Define the role of variables contained in the dataset to be analyzed. You may select one or more variables as target variables. These are the variables that corresponds with your business issue. The other variables of the table of data are considered to be explanatory variables: they allow calculation of the value of the target variable in a given context. They may also be used as weight variables.
C. Users are authenticated because clients must log in before being able to use the modeling server. User accounts can be configured to implement security policy.
D. Resources are used more fully because each modeling session has a dedicated process. The process size limit applies only to a single user
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