Why You Should Always Start With a Baseline Model

When we start a new ML project, we usually want to do the interesting stuff as soon as possible. Test the latest model we have read about. Test the state-of-the-art model that promises the best results. Do something complex with which we can show off in front of our colleagues and friends. Do the fun and challenging stuff.

It is easy to get carried away. We spend hours getting a model to run and finetune its hyperparameters.

But how do …

art colleagues machine learning model-evaluation project results show something state test