Results explanation

Per our 4Es framework used for this book, after we passed our model evaluation stage and selected the estimated and evaluated models as our final models, our next task for this project is to interpret the results to our clients.

In terms of explaining the machine learning results, the users of our project are particularly interested in understanding what influences the known rankings that are widely used. Also, they are interested in how new rankings are different from others and how the new rankings can be used.

So, we will work on their requests, but will not cover all of them as the purpose here is mainly to exhibit technologies. Also, for the confidentiality issue and also space limitations, we will not go into the details too much, but will focus more on utilizing our technologies for better explanations.

Overall, the interpretation is straightforward here, which include the following three tasks:

• Present a list of top-ranked schools and school districts

• Compare various...