July 3, 2026 · 4 min read
Why I always build a baseline model first
A complex model is only impressive relative to what a simple one achieves. Start simple, on purpose.
Machine Learning · Learning in Public
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It's tempting to reach straight for gradient boosting. But without a baseline, a 0.87 AUC is just a number, you can't tell whether the problem is hard or easy, or whether your feature work added anything.
What a baseline gives you
- A floor: majority-class or mean prediction tells you what 'no skill' looks like
- A sanity check: if logistic regression nearly matches the ensemble, the extra complexity isn't paying rent
- A debugging tool: when the fancy model underperforms the baseline, the pipeline is broken somewhere
The habit
Before any tuning: predict the majority class, then fit the simplest reasonable model on raw features. Log both scores. Every later experiment gets compared against them, and most of the time, the honest story of a project is written in those comparisons.