Comments (5)
Hi @arilwan ,
sorry for the late reply, I'm looking into this right now.
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I just checked the code, it seems to work (the sample code on the front page of the repo). Could you please share your code, and some details of the data? (How are the y labels represented, are they integers, or objects?)
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@gykovacs I must have bugs in my code. I can confirm all works well, closing.
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@gykovacs Great work! I want to compare some of the variants of SMOTE and I follow your Code smote_variants/examples/003_evaluation_one_dataset.ipynb and also looked some examples of your paper, but it has oversampled the entire dataset which should not be the case as it must be validated on testing data only.. Could you please provide me the code how I can compare Auc applying SMOTE, BorderlineSMOTE, AHC, CLuster SMOTE, ADASYN..Thanks in advance
Mail id: [email protected]
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@shwetashrmgithub I suggest you open a new issue since this issue is resolved and closed already.
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Related Issues (20)
- DEAGO : negative values for categorical features inside the data HOT 3
- Minimum number of rows in a class HOT 1
- when use SOMO,Why did the two types of samples not reach a balance and the number did not change HOT 2
- provided out is the wrong size for the reduction
- Categorical Variables HOT 1
- How to vary the "proportion" parameter - MulticlassOversampling class
- Why I get this error when I use smote_variants? HOT 9
- Could I apply this package to the time-series raw data?
- Question HOT 2
- Question: Combining these with Undersampling HOT 3
- Question: Regarding time complexity of Oversamplers and "Noise Filters" HOT 1
- GridSearchCV classifier parameters: int vs list HOT 3
- Implement 'verbose' parameter (feature request) HOT 2
- sv.MulticlassOversampling error for getattr() function HOT 2
- Error: Dimension of X_train and y_train is not the same ! HOT 2
- OversamplingClassifier does not work with probability-based metrics HOT 3
- Support for python 3.11 HOT 1
- Remove warnings
- Can smote_variants deal with 3_class data?
- I got this error when I used polynom_fit_SMOTE.
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