SMOTE (Synthetic Minority Over-sampling Technique) is an oversampling technique in the imbalanced-learn library in Python. It is used to address class imbalance in a dataset by generating synthetic samples from the minority class rather than replicating existing samples. This is done by randomly selecting a minority class sample and finding its k nearest neighbors, then creating synthetic samples by randomly selecting one of the neighbors and interpolating between the two samples. SMOTE helps to improve the performance of machine learning models when dealing with imbalanced datasets by increasing the representation of the minority class.
Python SMOTE - 60 examples found. These are the top rated real world Python examples of imblearn.over_sampling.SMOTE extracted from open source projects. You can rate examples to help us improve the quality of examples.