Defining Model Properties
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Under Primary Stages / Deployed
Stages / Machine Learning, click the
Random Forest Regression stage and drag it onto the
canvas, placing it where you want on the dataflow and connecting it to other
stages. Note that 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.
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Double-click the Random Forest Regression stage to show the Random
Forest Regression Options dialog box.
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Enter a Model name if you do not want to use the default
name.
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Optional: Check the Overwrite box to overwrite the
existing model with new data.
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Click the Objective field drop-down and select a numeric
field.
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Optional: Enter a Description of the model.
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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.
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Use the Model Data Type drop-down to specify whether
each input field is to be used as a numeric, categorical, or datetime field.
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Click OK to save the model and configuration or continue
to the next tab.