Comments (2)
sounds reasonable. i'm working on adding a configurable option now.
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you can now specify eval_kwargs = {'scale_x': False, 'scale_y': False} in the method file to set this. For KernelRidge scaling X is important, but we could probably turn it off for y
and for tree methods.
closing now, but feel free to open back up if needed.
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Related Issues (20)
- typo in feat_install.sh HOT 1
- Are these the same seeds used when running the competition? HOT 2
- Have you considered including TensorGP? HOT 3
- Quick start help needed - surely useful for other new users HOT 4
- Operon fails to launch from "evaluate_model.py" on Docker image HOT 26
- Always the same wrong result, across different methods *within SRBench*, when trying to fit a simple ground truth equation HOT 19
- Feature renaming issue HOT 1
- ImportError: cannot import name 'itea_srbench' from 'ITEA' HOT 13
- Invalid parameter alpha for estimator PySRRegressor(equations=0.0). HOT 3
- conda install .yml problem HOT 2
- New datasets + reorganization of current benchmarks HOT 5
- deprecated and broken algorithms HOT 5
- itea: error while loading shared libraries
- tir installation error
- run the ML method ------metadata.yaml not found HOT 6
- BSR-got an unexpected keyword "max_time" HOT 1
- where is the .src.ITEA? HOT 4
- Missing model_size HOT 4
- Singularity container for srbench HOT 1
- About Datasets HOT 2
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