Contributing more "building blocks"
Open
enhancement
- Dominant language
- Python
- Stars
- 8.1k
- Forks
- 1.3k
- PR merge metrics
- No merged PRs in 30d
Description
Hi AutoML team, I can chip in some code and add more algorithm "building blocks" to autosklearn. For example, I can add Factorization Machines for classification problems.
What is a good place to start with this? Any code-traditions that you would like me to follow?
Contributor guide
Research direction
The issue does not name a file, entry point, test, or established contribution path. Start by reviewing the auto-sklearn algorithm-component structure and existing classification components, then identify the project’s contribution guidance and tests; done would mean a Factorization Machines component is integrated and covered by the relevant tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100