smote_variants
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analyticalmindsltd
A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features
@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...
dear, presently I am working with large datasets with high dimensional (1459 features and 20 billion instances and using partial_fit method to execute my code. how could I use smote_variant...
I use Selection of the best oversampler to deal with 3_class data `from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier import smote_variants as sv import sklearn.datasets as datasets dataset= datasets.load_breast_cancer()...
I was checking the document's example of package. The following example gave me the error 'TypeError: getattr(): attribute name must be string'. Why? ``` import smote_variants as sv Import sklearn.datasets...
**I am getting this error when trying to use any sampler from smote_variants, my binary dataset has 30 input features and one output X_train is ndarray with shape (227845, 30)...
I use custom scorer from sklearn, via `make_scorer` function. It does not work if `needs_proba=True`, so metrics like ROC AUC, PR AUC are unfeasible to be used with `smote_variants`. The...
Thank you very much for providing the smote_variants package - an excellent tool! Seems that the **parameters** can not be passed as **lists**. I have a questions regarding parameter tuning...
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A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features