Apache Spark Machine Learning Blueprints

Results explanation

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.

Big influencers and their impacts

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 ...