scikit-learn-contrib/category_encoders

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

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#279 创建于 2020年10月30日

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 (11 评论) (2 反应) (1 负责人)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.

贡献者指南

技术栈
python
领域
datamachine learning
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
1-2 days
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
blocked
清晰度议题是否清楚说明期望改动、验收标准和下一步。
clear
前置要求
familiarity with category encodersunderstanding of CountEncoder.min group sizeknowledge of sklearn transformer APIPython
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
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.