Synthetic Minority Oversampling Technique (SMOTE) is an approach used for dealing with imbalanced datasets before running them through machine learning models.
Synthetic Minority Oversampling Technique (SMOTE) is an approach used for dealing with imbalanced datasets before running them through machine learning models. Common techniques for dealing with imbalanced datasets include over or under sampling either the minority or majority class. In this case, as the name suggests, SMOTE is a technique used to oversample the minority class. SMOTE can thereby create more balanced datasets that are less influenced by the majority class.
