JuliaAI / JuliaAI/MLJLinearModels.jl
Hardcoding of `Float64` in loss
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- Dominant language
- Julia
- Stars
- 86
- Forks
- 15
- PR merge metrics
- No merged PRs in 30d
Description
Is there a motivation for hardcoding the scaling in the loss penalties as Float64? If not, would a more generic definition allow for multiple types of regression outputs (Float64, 32, 16)?
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Research direction
Start at src/loss-penalty/generic.jl line 21 and trace how the Float64 scaling is used by the loss penalties. Determine whether the definition can support Float16, Float32, and Float64 regression outputs without breaking existing behavior. Done means the motivation is established and the genericity question is resolved in the implementation or issue discussion.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Refactor
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100