Introduction

Random Forest Classification enables you to perform machine learning by creating models from datasets that use continuous objectives with input variables.

To create your model, you must first complete the Model Properties tab. The Basic Options and Advanced Options tabs provide sufficient default settings to complete a job, but you can change those settings to meet your needs. You then run your job and a limited version of the resulting model appears on the Model Output tab; the complete output is available in the Machine Learning Model Management tool.
Note: For additional information, refer to this article about Distributed Random Forest (DRF) for additional information regarding Random Forest Classification and its options.