My first real model was a baseline classifier that I was fairly confident about. It performed badly — worse than just guessing the majority class every time.
My instinct was to see this as a failure to fix quickly and move past. Instead, I forced myself to slow down and actually diagnose why it failed, rather than just swapping in a different algorithm and hoping.
The real issue was class imbalance that I hadn't accounted for. The model had technically learned something — it had just learned that predicting the majority class was the "safe" answer almost every time, which made its accuracy look reasonable while being practically useless.
Once I saw that, the fix wasn't a fancier model — it was better framing of the problem: rebalancing the training data and picking a metric that actually reflected what I cared about, instead of plain accuracy.
The failed baseline taught me more than a working one would have. It forced me to understand what the model was actually optimizing for, instead of just trusting that a high accuracy number meant success.