scikit-learn-contrib/category_encoders

Feature request: min_group_size for other category encoders, e.g. one hot

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#279 建立於 2020年10月30日

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 (11 留言) (2 反應) (1 負責人)Python (2,322 star) (397 fork)batch import
enhancementgood first issue

描述

CountEncoder() has a min_group_size parameter that sets a minimum number of obs in a group required in order not for the group to be lumped together with other small groups. It would be nice to have this for the other encoder classes.

貢獻者指南

技術棧
python
領域
datamachine 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。
1-2 days
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。
blocked
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
familiarity with category encodersunderstanding of CountEncoder.min group sizeknowledge of sklearn transformer APIPython
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。
45
研究方向
The issue requests adding a min group size parameter to other encoders like OneHotEncoder. The implementer should study the CountEncoder's code in category encoders/count.py to understand the current implementation of min group size. Then, adapt the logic for each target encoder (e.g., OneHotEncoder in one hot.py) by adding a similar parameter and grouping logic. Existing tests in tests/ should be extended to cover the new parameter for each encoder. The discussion in the issue comments may provide additional context on desired behavior.