This case study describes a project with Toyota where we have constructed a machine learning web service that takes summary telemetry data as the input and assigns a risk rating to the trip.
In this real-life-code story, we show how we used Cognitive Services and LUIS to build a vehicle center console that can listen and respond to user's commands, specifically focusing on determining intent.
In this code story, we consider how data preparation can impact the performance of a classifier, and how that may lead to a refinement of problem statement, i.e. the important question we are asking.
An examination of whether a more sophisticated learner will always result in better performance in a text-based classifier, and the trade-off between accuracy and training time.