How to configure classification labels (based on return ranking) and adapt model training for classification tasks
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Description
❓ Questions and Help
How can I configure the label in the task config to generate discrete classification labels instead of continuous regression values? Specifically, I want to discretize the returns based on their cross-sectional ranking.
Once the labels are discrete, how should I configure the model and loss function to perform classification training?
And How should the backtesting module be adapted for classification outputs?
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Research direction
The issue names task configuration, model and loss-function settings, and the backtesting module but no files or tests. Trace those entry points to determine whether discrete labels and classification outputs are supported; done would require a documented configuration path and backtesting behavior, or a clearly scoped feature plan.
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Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100