stdlib-js / stdlib-js/stdlib

[BUG]: usage of camelcase for loss functions in `ml/incr/*`

Abierto Apto para principiantes
#13,359 2 comentarios 0 reacciones 0 asignados Ver en GitHub
Machine Learning
Lenguaje dominante
JavaScript
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Forks
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Merge medio
1 d 3 h
PR fusionados (30 d)
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Descripción

### Description

In continuation to this conversation [#13333](https://github.com/stdlib-js/stdlib/pull/13333#discussion_r3534668720), we noticed that the packages `ml/incr/binary-classification` and `ml/incr/sgd-regression` follow `camelCase` for mentioning loss functions, where as the current convention is to use `kebab-case`.

Expected outcome is to refactor all the loss functions from using camelcase to kebabcase.

### Related Issues

_No response_

### Questions

No.

### Demo

_No response_

### Reproduction

_No response_

### Expected Results

`ml/incr/binary-classification`
- `modifiedHuber` -> `modified-huber`
- `squaredHinge` -> `squared-hinge`

`ml/incr/sgd-regression`
- `epsilonInsensitive` -> `epsilon-insensitive`
- `squaredError` -> `squared-error`

### Actual Results

```shell

```

### Version

_No response_

### Environments

Node.js

### Browser Version

_No response_

### Node.js / npm Version

_No response_

### Platform

_No response_

### Checklist

- [x] Read and understood the [Code of Conduct](https://github.com/stdlib-js/stdlib/blob/develop/CODE_OF_CONDUCT.md).
- [x] Searched for existing issues and pull requests.

Guía de contribución

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Línea de trabajo

Start by inspecting the ml/incr/binary-classification and ml/incr/sgd-regression package entry points and searching their references for the four camelCase loss-function names. Rename each to its kebab-case form and verify that all package usage and tests consistently use modified-huber, squared-hinge, epsilon-insensitive, and squared-error.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
javascript
Área
machine-learning
Tipo de issue
Refactorización
Dificultad
2/5
Tiempo estimado
1-3 horas
Estado de actividad
Tranquilo
Claridad
Bien especificado
Aptitud para principiantes
72/100

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