In business, life, and friendships, we often look at what causes good and bad outcomes. When I was researching ways to make model performance better, I tried to understand the key features that help improve the model. Some people refer to this as Explainable AI (EXA), but I think of it as a basic approach in life.

I got this idea from a paper that questioned why transformers aren’t the best choice for time-series datasets. I used to believe that XGBoost was the best for tabular datasets, but after doing lots of tests, I found that transformers work better for time-series problems. However, there are some conditions to this finding. One thing I can publicly say is: the dataset should have many features (I found that more than 20, but ideally 50; and all features should relate to or ideally cause the target).

When I use AI to solve real-world problems, it’s important to understand what causes what. Mixing domain knowledge and creativity is key, but finding what causes what really makes a difference.

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