Hi All, Not sure if questions regarding the contributory packages are answered here. Just trying my luck.
I am have a seriously imbalanced classification problem. I am trying to use SMOTE+ENN oversampling and undersampling method to oversample my minority class and oversample my majority class. ======== from sklearn.datasets import make_classification from imblearn.combine import SMOTEENN sm = SMOTEENN() X, y = make_classification(n_classes=2, class_sep=2, weights=[0.2, 0.8], n_informative=1, n_redundant=1, flip_y=0, n_features=3, n_clusters_per_class=1, n_samples=50, random_state=10) X_df = pd.DataFrame(X) X_resampled, y_resampled = sm.fit_sample(X_df, y) ========= I understand that SMOTE returns a resampled data matrix i.e. X_resampled. I was wondering if there is a direct way to retrieve the indexes of the original data observations ? Thanks in advance. Best Regards and Seasons Greetings., Mamun
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