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Popular pages
- Welcome to imbalanced-learn documentation! — imbalanced-learn 0.3.0.dev0 documentation
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imbalanced-learn API — imbalanced-learn 0.3.0.dev0 documentation
Class to perform over-sampling using SMOTE. Combination of over- and under-sampling methods¶ The combine.SMOTEENN([ratio, random_state, ...]) combine.SMOTETomek([ratio, random_state, ...])
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imblearn.over_sampling.SMOTE — imbalanced-learn 0.3.0.dev0 documentation
Class to perform over-sampling using SMOTE. This object is an implementation of SMOTE - Synthetic Minority Over-sampling Technique, and the variants Borderline SMOTE 1, 2 and SVM-SMOTE. Parameters: Ra...
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imblearn.ensemble.EasyEnsemble — imbalanced-learn 0.3.0.dev0 documentation
Create an ensemble sets by iteratively applying random under-sampling. This method iteratively select a random subset and make an ensemble of the different sets. Parameters: Ratio to use for resamplin...
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imblearn.metrics.geometric_mean_score — imbalanced-learn 0.3.0.dev0 documentation
Ground truth (correct) target values. y_pred : ndarray, shape (n_samples, ) Estimated targets as returned by a classifier. labels : list, optional average != 'binary', and their order if average is N...
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General examples — imbalanced-learn 0.3.0.dev0 documentation
Example using ensemble class methods¶ Under-sampling methods implies that samples of the majority class are lost during the balancing procedure. Ensemble methods offer an alternative to use most of th...