It’s not how many times you get knocked down that count, it’s how many times you get back up.
As far as data scientist interviews go, discussing bias-variance tradeoff is one of the most common topics I have encountered, either as the person being interviewed in the past and more recently as the person interviewing the candidates or joining such interviews. Later in the post, we will discuss what bias-variance tradeoff is and why it works differently in deep learning exercises, but let me explain why I think this topic keeps coming up in determining the breadth of machine learning knowledge of data scientist candidates of both entry and experienced levels.
As machine learning scientists, we spend a great amount of time, energy, care and computational resources to train great machine learning models but we always know that our models will have a level of error as they generalize, which is also known as test error. Less experienced data scientists tend to focus on learning new modeling methodologies and algorithms, which I do believe is a healthy exercise. However, the more experienced data scientists are the ones who have learned over time how to better understand and handle the test error that inevitably exists in those trained models.