Defining Model Properties

  1. Under Primary Stages > Deployed Stages > Machine Learning, click the Random Forest Classification stage and drag it onto the canvas, placing it where you want on the dataflow and connecting it to other stages.
    Note: The input stage must be the data source that contains both the objective and input variable fields for your model; an output stage is not required unless you select the Score input data option on the Basic Options tab. You may also connect an output stage if you wish to capture your output independent of the Machine Learning Model Management tool.
  2. Double-click the Random Forest Classification stage to show the Random Forest Classification Options dialog box.
  3. Enter a Model name if you do not want to use the default name.
  4. Optional: Check the Overwrite box to overwrite the existing model with new data.
  5. Click the Objective field drop-down and select a numeric field.
  6. Click Multinomial levels and enter the maximum number of categories into which the objective field can be grouped. Note that checking this option will disable the Score input data option on the Basic Options tab.
  7. Optional: Enter a Description of the model.
  8. Click Include for each field whose data you want added to the model.
    Be sure to include the field you selected as the Objective field.
  9. Use the Model Data Type drop-down to specify whether each input field is to be used as a numeric, categorical, or datetime field.
  10. Click OK to save the model and configuration or continue to the next tab.