Calibration data
Calibration data is data used to measure, estimate, or adjust a model’s parameters, outputs, or behavior for a specified calibration objective.
Deep Learning with R, Third Edition
Representative data is data whose relevant properties and distribution resemble those of the environment and inputs encountered in production.
- How it is used
- Guides dataset collection so training inputs resemble the data encountered after deployment.
- How deep it goes
- Explains distribution mismatch through a concrete failure case and collection rule, without formal statistical criteria.
- Examples
- Uses dish photographs, IMDb reviews, Yelp reviews, and tweets to illustrate mismatched and matched data sources.
- What you can do after
- Evaluate whether collected data resembles production inputs and redirect collection toward the deployment environment.
Read how Deep Learning with R, Third Edition explains calibration data →
Deep Learning with Python, Third Edition
Data whose distribution matches the real-world inputs a model will encounter after deployment.
- How it is used
- Guides dataset collection toward inputs that resemble the model’s production environment.
- How deep it goes
- Explains distribution mismatch through a worked image example and recommends collecting data from the deployment environment.
- Examples
- Uses dish photographs, IMDB reviews, Yelp reviews, Twitter updates, and a historical sampling-bias example.
- What you can do after
- Apply it by collecting and annotating inputs from the expected deployment environment and user population.
Read how Deep Learning with Python, Third Edition explains calibration data →
Related concepts
- production datacontrasts with · Production data is data generated or collected through actual operational processes or real-world activity, rather than solely through artificial or controlled experimental settings.
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