Comments (2)
Hello! Yes the kappa score only works for categorical things. In fact we use a slightly modified kappa - the original kappa treats all categories as equal - i.e. getting the correct category is rewarded but incorrect categories are penalised equally. The linear weighted kappa ensures that categories that are close to the correct category e.g. 3-4 days instead of 2-3 days are "less bad" than predicting 8+ days. You can find more information about the linear weighted kappa score in this paper https://www.nature.com/articles/s41597-019-0103-9 as we used the same distribution bins as they did for ease of comparison. I hope that helps!
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Hello! Yes the kappa score only works for categorical things. In fact we use a slightly modified kappa - the original kappa treats all categories as equal - i.e. getting the correct category is rewarded but incorrect categories are penalised equally. The linear weighted kappa ensures that categories that are close to the correct category e.g. 3-4 days instead of 2-3 days are "less bad" than predicting 8+ days. You can find more information about the linear weighted kappa score in this paper https://www.nature.com/articles/s41597-019-0103-9 as we used the same distribution bins as they did for ease of comparison. I hope that helps!
ok, got it. Thanks for your kind reply!
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Related Issues (17)
- performance HOT 3
- Preproceesing eICU issue HOT 3
- Preprocessing MIMIC-IV issue HOT 1
- raise DataError('No numeric types to aggregate') pandas.core.base.DataError: No numeric types to aggregate HOT 2
- File "pandas\_libs\parsers.pyx", line 545, in pandas._libs.parsers.TextReader.__cinit__ pandas.errors.EmptyDataError: No columns to parse from file HOT 5
- AttributeError: 'Config' object has no attribute 'model_type' HOT 4
- Issues preprocessing MIMIC-IV using BigQuery HOT 1
- n_epochs does not work as an argument. HOT 1
- Channel mismatch in model_type 'tpc' HOT 1
- KeyError: '[141939 142056 142476 142521 142560 146391 147447 149039 149606 153006\n 160529 162431 166572 166709 167391 167417 171174 175528 177651 178069\n 178858 179142 179554] not in index' HOT 2
- Computation Power needed
- configuration of GPU machine for training? HOT 17
- Python Version HOT 2
- some conceptual questions about temp_pointwise HOT 9
- Some questions about performance HOT 19
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