What is smote definition?

SMOTE, which stands for Synthetic Minority Over-sampling Technique, is an algorithm used in machine learning to balance the class distribution of a data set with imbalanced classes. Imbalanced class distributions can be a significant problem when developing classification models. For example, if a classification model is used to predict fraud, but only 0.1% of the data set contains examples of fraud, then the model may be biased towards classifying examples as non-fraud, leading to poor performance on real-world data. SMOTE creates synthetic examples from the minority class by forming new examples between existing minority class examples. This technique raises the representation of the minority class, balancing the class distribution, thereby improving the accuracy of the model.