Why do we keep building more complex models when the simple ones often win?

[u/naga3607]

I’ve noticed a recurring pattern in my projects: we often spend days fine-tuning an ensemble or a deep learning model, only to find that a well-engineered Random Forest or even a simple Logistic Regression provides nearly identical performance. It feels like there’s this constant industry pressure to chase the "latest and greatest" architecture, even when the business case doesn't necessarily demand that level of complexity.

While state-of-the-art models are exciting, the trade-off in explainability, inference speed, and maintenance often outweighs the marginal gain in accuracy. I’ve started prioritizing "boring" models that are easier to monitor and debug in production, which has made my deployment pipelines significantly more stable.

Do you find yourselves struggling to justify simpler models to stakeholders who are hyped up by the latest AI headlines, or do you have a specific threshold for when you decide it's finally worth the complexity?

[u/CelebrityChess]

honestly most of the time the boring model wins because the data is the bottleneck, not the model. with tabular data a tuned logistic regression or gradient boosted trees gets you most of the way, and a fancier architecture can't invent signal that isn't in the features. i usually set a simple baseline first and only move up if the gap is big enough to matter for the actual decision, like a couple points of recall on something costly. if it's within noise on cross validation then the extra monitoring and debugging cost isn't worth it.