High rmse with excellent AR
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Description
❓ Model biases with XGBoost
I have been tuning my XGBoost hyperparameters. I have very poor rmse' on both train and validation but excellent backtesting results. Essentially those also convert well into paper trades on long positions.
I have imputed some portions of my data to cover missing volume numbers in certain years. This although has been done using interpolation as a series.
I'm trying to figure out anty biases the model might have(or rather I might have) with such a situation. Thoughts appreciated.
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
No files, tests, or reproducible configuration are named. Start by comparing the training and validation RMSE setup with the backtesting and paper-trading setup, then examine how interpolated missing volume data enters each path. Done would require a reproducible diagnosis of the discrepancy and a clearly scoped correction.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100