liveBook · Manning

Confidence interval

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

A confidence interval is an interval estimate expressing uncertainty about an unknown quantity at a specified confidence level.

A confidence interval expresses uncertainty around a model’s estimate at a specified confidence level. In demand forecasting, it gives decision-makers a range rather than only a point prediction. section 8.5

Use confidence intervals as part of the model delivery contract:

  • Require every model output to include a confidence interval. section 8.5
  • Expose intervals in counterfactual what-if scenarios and prebuilt scenarios so users can judge decision uncertainty. section 8.5

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

How deep it goes
Requires confidence intervals as model outputs but does not specify their calculation or statistical interpretation.
Examples
Uses what-if scenarios and prebuilt scenarios to present prediction ranges for business decisions.
What you can do after
Apply it through model outputs to compare scenarios while accounting for prediction uncertainty.
How it connects
Connects confidence intervals with demand forecasting, counterfactual analysis, and performance monitoring.

Also explained in

Compare how 10 books explain confidence interval →

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.