tensorflow / tensorflow/tensorboard
Ignore outliers affects scaling but not smoothing
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- Dominant language
- TypeScript
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Description
The "ignore outliers" is feature is a great idea. Most training curves start from a nonsensical first point that is best ignored. However, it should also apply to smoothing. In its present implementation, you get correct scale but a "smoothed" curve that still starts at the nonsensical first point and slowly descends towards real data, even if in reality, it had only one initial nonsensical point and descended towards normal values immediately.
An easier heuristic-free implementation would be to just ignore all data points before N. For scaling, smoothing, everything. Also, the first few points are always nonsensical but I usually do care about outliers that happen during training.
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Research direction
The issue concerns training-curve scaling and smoothing, but names no source files, tests, or entry points. Trace how the "ignore outliers" option is applied to scaling and smoothing, then verify that the same initial points are excluded from both and that later training outliers remain visible.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- typescript
- Domain
- data-visualization, frontend
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 42/100