Your Model Is 94% Accurate. It's Also Making Terrible Decisions.

Your model hit 94% accuracy. And then it made the worst possible decision in production. Here's what nobody tells you about building ML systems that actually work. The Problem Nobody Talks About You train the model. You evaluate it. The numbers look great. Then it goes live and starts recommending things that make zero sense in the real world. Not because the math is wrong. Because accuracy and decision quality are not the same thing. A model can be statistically excellent and practically useless. Worse, it can be confidently wrong, which is the most dangerous state in any automated decision system. What Most Projects Get Wrong Most ML projects stop here: raw data → model → prediction → done That's not a decision system. That's a calculator with good PR. A real decision system looks more like this: raw data → feature engineering → model → explanation → decision logic → scenario simulation → outcome Notice what's in the middle: explanation and scenario simulation. That's where the real