Comments (2)
That's right. I knew this was an issue, just never fixed. I will sort that out in the next day or so.
SVM_SMOTE doesn't exist anymore, it is now one of four options of the SMOTE class (regular, borderline 1, 2 and SVM), which are controlled by the parameter kind
.
As for SMOTETomek I'm not sure what's going on. I will try to investigate, but it may take a little while since I didn't write the code for it, so I'll have to familiarize my self with it first.
Thanks for the comments, and keep them coming!
from imbalanced-learn.
See 08df1f5.
from imbalanced-learn.
Related Issues (20)
- Compatibility with scikit-learn 1.4.0 HOT 3
- [BUG] `sklearn=1.4` TypeError: BaggingClassifier.__init__() got an unexpected keyword argument 'base_estimator'
- [BUG] Attribute Error: Pipeline object has no attribute "_check_fit_params" HOT 1
- [BUG] Test issues with sklearn 1.4 HOT 3
- [SO] SMOTEEN generates imbalance dataset HOT 2
- AttributeError: 'NoneType' object has no attribute 'split' HOT 3
- [BUG]sampling_strategy working incorrectly with random oversampling HOT 2
- SmoteNC leads to Killed process HOT 2
- [BUG] ImportError: cannot import name '_check_X' from 'imblearn.utils._validation' (/usr/local/lib/python3.10/dist-packages/imblearn/utils/_validation.py) HOT 3
- fix scikit-learn 1.5 parse_version link HOT 1
- Python 3.13: Two tests from test_docstring.py are failing HOT 2
- New error message from established workflow: AttributeError: 'Pipeline' object has no attribute '_check_fit_params'[BUG] HOT 8
- how can the tool be used with DASK-ML to deal with large data HOT 1
- [BUG] ImportError: cannot import name '_get_column_indices' from 'sklearn.utils with pre-release versions HOT 1
- [BUG] ImportError: cannot import name '_get_column_indices' HOT 8
- Combine SMOTENC and TomekLink and Classifier together in a pipeline for Mixed Datatype Datasets
- [BUG]ImportError: cannot import name '_get_column_indices' from 'sklearn.utils' HOT 5
- Applying sampling method to sensitive features for fairness models HOT 3
- Unable to achieve balanced samples for each estimator (Balanced Bagging Classifier) HOT 2
- model hyperparameters be adjusted before and after oversampling? HOT 1
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