Quantile Regression
Definition
Estimate conditional quantiles instead of just average values.
Quantile regression models a chosen quantile of a target variable depending on input features, for example, an upper threshold of expected delivery times.
Simply explained
Instead of a single center line, different ranges of possible values are described.
Why it matters in practice
Different risks and dispersions can be considered in forecasts.
Example from business
A hypothetical retailer estimates not only typical delivery times but also their upper quantile.
Limits and pitfalls
- Estimated quantiles may be insufficiently calibrated; a range is not a guaranteed promise.
Origin & history
The method extends regression models to conditional distribution quantiles.
Use in mid-sized companies
- Use separate test data to check how often observations fall below the estimated quantile.
Frequently asked questions
Is this the same as a confidence interval?
No, the terms describe different uncertainties.
Sources
Editorially reviewed on: