![]() Target variable: Displays a summary spreadsheet of best predictors, a MD values graph and a spreadsheet with MD values for selected predictors, a matrix scatterplot, a spreadsheet of eliminated predictors, and a spreadsheet of predictors remaining at this step.Data redundancy: Displays a correlation matrix for all inputs, and a spreadsheet that contains the name of the redundant variables, roles and redundancy criterion and other related information.It also displays a spreadsheet with information about the inputs and targets specified for the analysis. Data for analysis: Displays a spreadsheet of basic statistics (for example mean, standard deviation, skewness, kurtosis, min, max) for all continuous inputs and targets.For each selected variable the variable number, name, long name and type (continuous or categorical) is reported. Data preparation: Displays a spreadsheet of the variables selected for analysis.Results for each step are described as follows: If the step is complete, the status of the step is changed fromīutton when the step is not complete, a message is displayed prompting you to complete the step. If required, the initial state of a step isīutton. Note that not all steps have three states. Below is a summary list of the Data Miner Recipe steps and the states in which they can exist. Therefore, you can only start the Target variables step when you have successfully completed the Data preparation and Data for analysis steps. For example, in any model building task, data are used as examples for the model to learn the underlying process relating the input and target variables. This ensures that all the information required for successful completion of any given step is in place when the step is started. The Data Miner Recipes steps are arranged in a logical and sequential order. The change is made if the step is complete.
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