Ranking algorithm for the layman – New Modeler Extension

There are a lot of ranking algorithms for you to use and represent your data. Now, that doesn’t mean that the ranking algorithm is the most appropriate for your situation. It all depends on what you are trying to accomplish and who will be the audience of your final product. Some ranking algorithms could be too sophisticated to be actually understood by a layman’s type of person. The reasoning to develop this particular piece of work was to not only rank specific type of datasets but be able to explain how the ranking is performed for the layman’s type of person.


The inspiration for creating this node came from this site -> http://www.psychstat.missouristate.edu/introbook/sbk14.htm. Now that this node exists, it allows us to reuse it over and over. According to the author of the site, transforming raw scores into percentile ranking, will enable us to: 1) Give us meaning and interpret the scores and 2) Provide a direct comparison between scores.

Sample Output:

If you want to learn more and try this extension go to the GitHub Repository. You can find my extension and all others in thePredictive Analytics Gallery.

Source: Missouri State – Score Transfromations

Source: | Ranking algorithm for the layman – New Modeler Extension

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