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imbalanced-learn

Toolbox for handling imbalanced datasets in machine learning

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Toolbox for imbalanced dataset in machine learning

Commands

imbalanced-learn

Examples

Import and use SMOTE for oversampling minority class

$ python -c "from imblearn.over_sampling import SMOTE; smote = SMOTE(); X_resampled, y_resampled = smote.fit_resample(X, y)"

Apply RandomUnderSampler to balance dataset classes

$ python -c "from imblearn.under_sampling import RandomUnderSampler; rus = RandomUnderSampler(); X_resampled, y_resampled = rus.fit_resample(X, y)"

Create pipeline combining preprocessing and classification

$ python -c "from imblearn.pipeline import Pipeline; from imblearn.over_sampling import SMOTE; from sklearn.ensemble import RandomForestClassifier; pipe = Pipeline([('smote', SMOTE()), ('clf', RandomForestClassifier())])"