In this post, we summarize questions and answers from GTC sessions with NVIDIA’s Kaggle Grandmaster team. Additionally, we answer audience questions we did not get a chance during these sessions.
Ahmet: I read the competition description and evaluation metric. Then I give myself several days to think about if I have any novel ideas that I can try on. If I do not have any interesting ideas, then I do not join. But sometimes I just join for learning and improving my skills.
Kazuki: Not mandatory, but you may want to understand the competition metric and how machine learning models work. For example, the linear model and tree model are totally different. So those would generate good results when ensembling.
Bojan: On the first day, I always submit a sample so that I am on the leaderboard. Traditionally, I have not been very big on data analysis or EDA, which is one of my weaknesses. But recently, I started doing more and changing my approach.
One thing I always do is see how easy it is to ensemble different models in a competition. This dictates my strategy in the long run. If ensembling slightly different models can give a nice boost, it means that building many diverse models is important. However, if ensembling does not give you a big boost, then feature engineering or coming up with creative features is more important in the long run.