Investigate which methods can be adapted to use quality flags for different weights
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Description
An advantage of MOGPR is its provision of uncertainty intervals. So far, these uncertainties have not been fully exploited. It could be explored to implement a quality flag (QF) based on these uncertainty estimates, e.g., when exceeding a given threshold. Such QF can then be used as a mask so that only reconstructed values are provided that fall within a given uncertainty interval.
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
The issue does not name files, tests, or an entry point. Start by locating the MOGPR implementation and reviewing how its uncertainty intervals are produced; the work would need to define threshold-based quality flags and masking behavior for reconstructed values, then document or test the resulting behavior.
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Assessment
- Tech stack
- machine-learning
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100