Your First Model Is Going to Be Embarrassing - And That's Okay
TL;DR: Before anything clever, fit a model that guesses the majority class and a plain logistic regression. Together, they take seconds, and they're the only way to tell what the expensive model actually bought you. On six public datasets, guessing alone was 95.3% accurate on hypothyroid.
Default boosted trees beat logistic regression by 2 to 15 AUC points. A 200-fit hyperparameter search added more than half a point on only one dataset, and across four random splits its gain on the other five averaged between −0.3 and +0.2 points. Skip the cheap models, and the complex one takes credit for all of it.
Ask an AI coding assistant to "build a model to predict churn" from a CSV, and you'll often get a notebook that looks finished: preprocessing, a gradient-boosted model, a hyperparameter search, a classification report, maybe a feature-importance chart. Before you read any of the numbers, search the...
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