Monte Carlo Dropout
Definition
Monte Carlo Dropout uses repeated, slightly altered model runs for uncertainty estimation.
An approximation method for uncertainty estimation that activates dropout during multiple inference runs.
Simply explained
The model responds multiple times with partially disabled connections; differences provide clues about uncertainty.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
A model generates twenty predictions; their spread serves as an uncertainty signal.
Limits and pitfalls
- Treating spread as calibrated probability or overlooking runtime costs.
Origin & history
Gal and Ghahramani interpreted dropout in 2016 as approximate Bayesian inference.
Use in mid-sized companies
- Possible application: A model generates twenty predictions; their spread serves as an uncertainty signal.
Frequently asked questions
What does Monte Carlo Dropout mean?
An approximation method for uncertainty estimation that activates dropout during multiple inference runs.
Which mistakes should be avoided?
Treating spread as calibrated probability or overlooking runtime costs.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: