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.