Introduction to Model Scoring

Model Scoring enables you to score new data using the formula created when you fit a machine learning model.

Note: Models must first be exposed through the web interface before they will appear in the Model Scoring stage.

To score your data, you must complete two tabs of the Model Scoring Options dialog. First identify the model and its type, and then ensure the model's fields are correctly mapped to Spectrum Technology Platform fields. Following that, you configure the output by selecting which fields you want to include and running your job. The output will appear on the Model Output tab.

After running your job, you will need to send the scores to an output table and then run the dataflow or web service.