scikit-learn-contrib/category_encoders

Metadata for encoders

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#166 opened on Jan 5, 2019

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 (10 comments) (2 reactions) (0 assignees)Python (2,322 stars) (397 forks)batch import
enhancementhelp wanted

Description

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.

Contributor guide

Tech stack
python
Domain
machine learning
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
Python programmingscikit learn API knowledgecategory encoders library structure
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
70
Research direction
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.