Baseline Heuristics#
Simple rules used as reference points to judge whether a model adds value.
Important
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What it is#
Baseline heuristics are the simple, naive reference models a real system must beat to earn its complexity — the “dumb” benchmark. Predicting the mean or median, the majority class, the last value, or a basic if-then rule are all baselines.
Why they’re essential#
A metric is meaningless in isolation — 90% accuracy is trivial if the majority class is already 90%. A baseline sets the floor: if a complex model can’t beat it, the model adds no value and may even hide a bug. Baselines are cheap, fast, and interpretable, so they cost almost nothing to run.
Where they show up#
Baselines frame every honest evaluation and are built into metrics — MASE, for instance, divides a forecast’s error by a naive baseline’s, so a score below 1 literally means “better than the heuristic.” Always establish the baseline first.
Theme: Model Evaluation & Uncertainty · All terminology
Hint
Mind map — connected ideas
MASE (Mean Absolute Scaled Error) · Root Mean Squared Error (RMSE) · Accuracy · Mean Absolute Error (MAE) · Time Series Forecasting · Model Score
Hint
More in Model Evaluation & Uncertainty
Average Absolute Error (AAE) · Bootstrap · Bootstrap Confidence Intervals (CIs) · Coverage · Cramér’s V · DeLong’s Test · KS Statistic (Kolmogorov–Smirnov Statistic) · Likelihood Ratio (LR) · Mann–Whitney U Test (also called the Wilcoxon rank-sum test) · MASE (Mean Absolute Scaled Error) · Mean Absolute Error (MAE) · Mean Absolute Percentage Error (MAPE) · Mean Squared Error (MSE) · Relative accuracy
See also
Source article Adapted (context, re-expressed) in our own words from: Baseline Heuristics (insightful-data-lab.com).