Scoring Outside the Box
Nascif Abousalh-Neto, JMP Principal Software Developer, SAS
Daniel Valente, PhD, JMP Senior Product Manager, SAS
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Scoring – the process of using a model created by a data mining application like JMP to make predictions for new data – has been called the "unglamorous workhorse of data mining." Like a dark yin to the bright yang of predictive modeling, scoring plays a fundamental role in the implementation of a complete data mining life cycle. Scoring requires that the model is first adapted so that it can run where the new data is produced or stored. This process is usually a time-consuming and error-prone endeavor. In this paper, we will see how the new score code generation features in JMP 13 can assist you in extending the reach of your models while minimizing the work required to adapt them.
Nascif Abousalh-Neto, JMP Principal Software Developer, SAS
Daniel Valente, PhD, JMP Senior Product Manager, SAS
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Scoring – the process of using a model created by a data mining application like JMP to make predictions for new data – has been called the "unglamorous workhorse of data mining." Like a dark yin to the bright yang of predictive modeling, scoring plays a fundamental role in the implementation of a complete data mining life cycle. Scoring requires that the model is first adapted so that it can run where the new data is produced or stored. This process is usually a time-consuming and error-prone endeavor. In this paper, we will see how the new score code generation features in JMP 13 can assist you in extending the reach of your models while minimizing the work required to adapt them.