How do you explain scoring to your boss?
What is model scoring vs training?
What is scoring code?
Why is it generated?
What is the goal of analytical modeling?
The answers may depend upon your point of view.
Building a good predictive model is a complex, multi-stage process and is often referred to by the statisticians in our midst as training or fitting a model.
Training a model can involve a variety of different algorithms or ways of examining the data in order to discover implicit relationships. Developing analytical models requires selecting the most appropriate algorithm or combination of algorithms for a given situation. In this, expert knowledge and experience can be critical in developing the very best possible model.
This can lead some statisticians to be quite passionate, even poetic about the elegance of one algorithm compared to another. Such issues can indeed be important. But the person who signs the check for salaries, software and systems doesn’t really care about that. He cares about results.
In the process of analytical modeling, those results come from applying what was learned in training to new data to either classify the new data or provide estimates based on the new data. Another way to think of it is the new data is tested with or compared to the information saved in training and score from that “test” provides the results or scores, hence the euphemism, scoring.
If your situation allows access to the new data that needs to be scored on the same machine where the model was trained it often takes little more than an option or the addition of new node to apply the model. If as is often the case the new data is on a different “production” system that may not have access to all the tools available on the training system you need a different way to apply your model.
There was a time when after all the work of training the model you had to then use the training results often little more that a list of numbers to hand code a program that could run standalone on the “production” system. Of course, after you coded the scoring program you had to test it, and fix it, and test it, etc. With SAS® Enterprise Miner™ all of that is done for you and it produces a fully tested program designed by some of the foremost experts in the field to apply the model or score new data.
The score code generated by Enterprise Miner gives you and easy dependable and accurate way to deploy complex and powerful analytical models on any of your or your customers’ SAS systems regardless of whether or not they have access to Enterprise Miner.
Look for more discussions of scoring in the future. For example, have you ever wondered what some of the output variables generated by Enterprise Miner score code represent?
"Prediction is very difficult, especially if it's about the future."
~ Niels Henrik David Bohr (1885-1962) Danish physicist and Nobel laureate
