Mission accomplished
The mission was to train models that could predict preventable delays with enough accuracy to be useful, and then, to understand the factors that impacted these delays, according to these models, to improve OTP. The resulting regression models all predicted delays, on average, well below the 15-minute threshold according to the RMSE. And most of the classification models achieved an F1 score well above 50% – one of them reached 98.8%! We also managed to find factors that impacted delays for all white-box models, some of which performed reasonably well. So, it seems like it was a resounding success!
Don’t celebrate just yet! Despite the high metrics, this mission was a failure. Through interpretation methods, we realized that the models were accurate mostly for the wrong reasons. This realization helps underpin the mission-critical lesson that a model can easily be right for the wrong reasons, so the question “why?” is not a question...
