After we passed our model evaluation stage and decided to select the estimated and evaluated model as our final model, our next task is to interpret results to the company executives and technicians.
Here, we will work on results explanation with a focus on large influencing variables.
As we briefly discussed before, quality and freshness are very different for each dataset. Each data has its own weakness, as summarized in the following:
Category |
Weakness |
---|---|
Web Log |
incomplete |
Account |
old |
Computer device |
incomplete |
User |
old |
Business |
Incomplete and old |
Due to the preceding issues, we often do not have enough data to score each transaction or score it with good accuracy, and we can only score it later. Because of this, the company hopes to identify some special signals or insights that can be used to take action quickly and easily.
The following briefly summarizes some of the result samples that we use some functions from randomForest...