The imblearn.over_sampling.SMOTE.SMOTE is an oversampling technique used in Python for imbalanced datasets. SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class by selecting similar instances and creating new instances along the line segments joining them. This technique helps address the class imbalance problem by increasing the number of instances in the minority class, thus improving the performance of machine learning models trained on imbalanced datasets.
Python SMOTE.SMOTE - 30 examples found. These are the top rated real world Python examples of imblearn.over_sampling.SMOTE.SMOTE extracted from open source projects. You can rate examples to help us improve the quality of examples.