Ask better questions
If you are working on the wrong problem, the quality of even the best neural networks, probabilistic and gradient boosted models would be useless. However, in data science, it is assumed that the problem being worked on is always clear. It is not.
In his post Tukey, Design Thinking, and Better Questions, Roger Peng points to the importance of asking better questions as a data scientist.
The best neural networks, probabilistic and gradient boosted models are worthless, if you are working on the wrong problem.
But in data science the problem should always be clear, right?
Quite the opposite. Let's consider the following scenario:
Within the first week after construction the new loading dock gets flooded. Your model's prediction was way off – Where did it go wrong?
The theme of the post gives away what the answer to this riddle might be: Somehow, this is about the question you did (or did not) ask yourself when working on this project.
In fact, it is about a buried question. 🍃