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Baseline Models: Why You Always Need One

A simple baseline turns a metric into a meaningful result. The baselines to use for common problem types.

Editorial team 1 min read

A metric on its own means little. Is 92% accuracy good? It depends on how well a trivial approach would do.

What a Baseline Is

A baseline is the simplest reasonable way to solve the problem. Every model must beat it, and by enough to justify its complexity.

Baselines by Problem Type

  • Classification: always predict the most common class; or a simple rule an expert would use.
  • Regression: predict the mean or median of the training target.
  • Time series: the last value (naive) or the value one season ago (seasonal naive).
  • Ranking and recommendations: most popular items.
  • Text classification: keyword rules or a bag-of-words logistic regression.
  • Existing process: whatever people or systems do today.

scikit-learn's DummyClassifier and DummyRegressor provide trivial baselines in one line.

What Baselines Reveal

  • If a complex model barely beats the baseline, the features may lack signal.
  • If the baseline is already good enough, you may not need machine learning.
  • A strong improvement over the baseline is a compelling, easy-to-explain result.

Report Them

Always present results side by side: "The model's MAE is 180 units; the seasonal naive forecast's is 260." This makes the value clear to non-specialists.

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