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Baseline model

As explained in Delivering Machine Learning Projects: From design to deployment

A benchmark model is a baseline model whose results provide a reference for comparing alternative models.

A benchmark model is a baseline whose results provide a reference for comparing alternative models during structured evaluation. section 8.5

Use the benchmark to:

  • Quantify whether a corrected or enhanced model delivers a meaningful performance improvement. section 8.5
  • Support model-portfolio evaluation in a test environment before production release. section 8.5

Bike Shop demand prediction

After an obsolete training-pipeline stub is fixed, the enhanced model shows a significant performance uptick compared with the benchmark model, prompting formal portfolio evaluation. section 8.5

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes
Defines comparative use and shows evaluation in a test environment, but does not detail benchmark construction or metrics.
Examples
Uses a Bike Shop demand model whose corrected implementation improves significantly against the benchmark.
What you can do after
Evaluate alternative models by comparing their performance with a stable baseline before production release.
How it connects
Connects model evaluation, model portfolios, training pipelines, and test environments.
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.