For a data scientist building predictive models, the following are important:
- How good is the model ?
- How good is it compared to competing/alternate models?
- Is there a way to identify what worked in the models built so far, to leverage it to build something even better?
The stakeholder/end-user who finally uses the output from the model, for whom the ML process is mostly black-box, is concerned with the following:
- How to trust the model output?
- How to understand the drivers?
- How to do what-if analysis?
The unifying theme that could answer most of the above questions is visualization. The biggest challenge is to find a way to visualize the model, the model fitting process and the impact of drivers. This talk summarizes the learnings and key takeaways when communicating model results.