JuliaAI / JuliaAI/MLJTransforms.jl
`asinh` transformation
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- Dominant language
- Julia
- Stars
- 1
- Forks
- 2
- PR merge metrics
- No merged PRs in 30d
Description
Similar to the Box-Cox transformation, the asinh or pseudolog transformation is a common transformation for reducing skewness and stabilizing variance. It's most often used for variables that are roughly log-normal, but can take on both positive and negative values; for example, net worth is often well-modeled as log-normal for the majority of the population, but can be negative if debts exceed assets. asinh(x/2) is approximately equal to ln(|x|) for large values of |x|, but is approximately equal to x for values close to 0.
The general form of the transformation is x = scale * asinh(x / (2scale)), with scale a parameter chosen to satisfy some criterion such as stable variance, minimum skewness, or maximizing the log-likelihood that the data come from a normal distribution.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No source file or test is named. Start by locating the existing Box-Cox transformation and its tests, then determine how an asinh transformation and its scale parameter should fit the package API. Done means the transformation is available with documented scale behavior and has tests covering positive, negative, and near-zero values.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100