Explanations of the results

As before, after we have passed our model evaluation stage and decided to select the estimated and evaluated model as our final model, our next task is to interpret the results for the city management and technicians.

In terms of explaining the machine learning results, the city is particularly interested in understanding what factors influence the service request number and how service requests change over time.

So, to serve the city governments and other interested civic organizations, we need to set our focus on further deriving results about big influencing variables and time series trends with our final models. Then, we need to work on interpretations as well as some visualizations as R provides many good visualization solutions.

Biggest influencers

In terms of finding out the features with the largest impact on the target feature, as you learned from the previous chapters, the random forest method is a good solution. Therefore, once our Zeppelin notebook is...