scikit-learn-contrib/category_encoders

Metadata for encoders

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#166 建立於 2019年1月5日

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 (10 留言) (2 反應) (0 負責人)Python (2,322 star) (397 fork)batch import
enhancementhelp wanted

描述

It should be possible to programmatically differentiate between encoders that:

  1. Do not require any target during fitting (like OneHotEncoder).
  2. Require some target during fitting (like TargetEncoder).
  3. Require binary target during fitting (like WOE).

This information would be useful for:

  1. Parameterized tests.
  2. Users that wonder, which encoder they may use.

Proposed implementation: Like in scikit-learn.

貢獻者指南

技術棧
python
領域
machine learning
議題類型
feature
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
3
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
half day
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
stale
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
clear
前置要求
Python programmingscikit learn API knowledgecategory encoders library structure
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
70
研究方向
Examine the existing encoder classes in category encoders to identify their target requirements. Look at scikit learn's Estimator type tags for inspiration on how to implement a metadata system. Review the issue comments for additional design considerations and potential pitfalls. The implementation could involve adding a class attribute or method to each encoder indicating target dependency.
Metadata for encoders · scikit-learn-contrib/category_encoders#166 | Good First Issue