Baseline Comparison Review

Purpose

Machine learning models add technical debt. A complex model earns its place only by clearly outperforming a "dumb" baseline; this review makes that comparison explicit before a new model is approved.

When to Use

• A new model architecture is proposed for approval
• A complex model would replace an existing heuristic or rule
• Reported gains have not been compared against any baseline

Inputs

• The candidate model's evaluation results (metric + data split)
• The evaluation code, or enough dataset/task detail to define fair baselines

Workflow

1. Define the naive baseline:
   • Classification: predict the majority class.
   • Regression: predict the mean or median of the training target.
   • Time series: predict the last known value (naive persistence).
2. Define the heuristic baseline: what simple if/else rule would a domain expert write?
3. Evaluate both baselines on the same split and metric as the candidate model.
4. Evaluate the delta: if the complex model only beats the heuristic baseline marginally (e.g., ~1%), recommend keeping the heuristic — the complexity is not worth the maintenance cost.
5. Demand a baseline evaluation script before approving the new architecture, so the comparison is rerunnable.

Output

• A baseline-vs-model comparison on identical data and metric, with an explicit keep/replace recommendation that weighs maintenance cost

Verification

• Naive baseline defined and evaluated
• Heuristic baseline defined and evaluated (or explicitly ruled out with a reason)
• Candidate compared on the same split and metric as the baselines
• Delta judged against maintenance cost, not just statistical improvement
• A rerunnable baseline evaluation script exists

Failure Modes

• No heuristic baseline — comparing only against the naive baseline makes weak models look strong. Ask what rule a domain expert would write.
• Unequal comparison — baseline evaluated on a different split or metric than the model. Re-run both on identical data.
• Complexity bias — approving a model for a marginal gain without stating the maintenance cost in the recommendation.