Results explanation

After passing our model evaluation stage, and deciding to select the estimated and evaluated model as our final model, our next task is to interpret the results for the company executives and technicians.

In terms of explaining the machine learning results, the company is particularly interested in understanding how their past interventions affected customer churns, and also how their product features and services influence customer churns.

So, we will work on results explanation, focusing on calculating the effects of several interventions or some product and service features for which MLlib does not offer good functions now. Therefore, in reality, we export the estimated models, and use other tools for results explanation and visualization. However, it is expected that the future releases of MLlib will have these easy functions included soon.

Calculating the impact of interventions

With logistic regression, the process of producing scores is relatively easy; it uses the...